Overview

Dataset statistics

Number of variables111
Number of observations39717
Missing cells2263364
Missing cells (%)51.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.6 MiB
Average record size in memory888.0 B

Variable types

Numeric25
Categorical30
Boolean2
Unsupported54

Warnings

pymnt_plan has constant value "False" Constant
initial_list_status has constant value "False" Constant
collections_12_mths_ex_med has constant value "0.0" Constant
policy_code has constant value "1" Constant
application_type has constant value "INDIVIDUAL" Constant
acc_now_delinq has constant value "0" Constant
chargeoff_within_12_mths has constant value "0.0" Constant
delinq_amnt has constant value "0" Constant
tax_liens has constant value "0.0" Constant
int_rate has a high cardinality: 371 distinct values High cardinality
emp_title has a high cardinality: 28820 distinct values High cardinality
issue_d has a high cardinality: 55 distinct values High cardinality
url has a high cardinality: 39717 distinct values High cardinality
desc has a high cardinality: 26527 distinct values High cardinality
title has a high cardinality: 19615 distinct values High cardinality
zip_code has a high cardinality: 823 distinct values High cardinality
earliest_cr_line has a high cardinality: 526 distinct values High cardinality
revol_util has a high cardinality: 1089 distinct values High cardinality
last_pymnt_d has a high cardinality: 101 distinct values High cardinality
last_credit_pull_d has a high cardinality: 106 distinct values High cardinality
id is highly correlated with member_idHigh correlation
member_id is highly correlated with idHigh correlation
loan_amnt is highly correlated with funded_amnt and 2 other fieldsHigh correlation
funded_amnt is highly correlated with loan_amnt and 3 other fieldsHigh correlation
funded_amnt_inv is highly correlated with loan_amnt and 3 other fieldsHigh correlation
installment is highly correlated with loan_amnt and 2 other fieldsHigh correlation
out_prncp is highly correlated with out_prncp_invHigh correlation
out_prncp_inv is highly correlated with out_prncpHigh correlation
total_pymnt is highly correlated with funded_amnt and 2 other fieldsHigh correlation
total_pymnt_inv is highly correlated with funded_amnt_inv and 2 other fieldsHigh correlation
total_rec_prncp is highly correlated with total_pymnt and 1 other fieldsHigh correlation
delinq_amnt is highly correlated with policy_code and 20 other fieldsHigh correlation
policy_code is highly correlated with delinq_amnt and 20 other fieldsHigh correlation
pub_rec is highly correlated with delinq_amnt and 8 other fieldsHigh correlation
purpose is highly correlated with delinq_amnt and 8 other fieldsHigh correlation
next_pymnt_d is highly correlated with delinq_amnt and 10 other fieldsHigh correlation
initial_list_status is highly correlated with delinq_amnt and 20 other fieldsHigh correlation
collections_12_mths_ex_med is highly correlated with delinq_amnt and 20 other fieldsHigh correlation
application_type is highly correlated with delinq_amnt and 20 other fieldsHigh correlation
verification_status is highly correlated with delinq_amnt and 8 other fieldsHigh correlation
grade is highly correlated with delinq_amnt and 9 other fieldsHigh correlation
pymnt_plan is highly correlated with delinq_amnt and 20 other fieldsHigh correlation
acc_now_delinq is highly correlated with delinq_amnt and 20 other fieldsHigh correlation
addr_state is highly correlated with delinq_amnt and 8 other fieldsHigh correlation
sub_grade is highly correlated with delinq_amnt and 9 other fieldsHigh correlation
emp_length is highly correlated with delinq_amnt and 8 other fieldsHigh correlation
issue_d is highly correlated with delinq_amnt and 8 other fieldsHigh correlation
term is highly correlated with delinq_amnt and 9 other fieldsHigh correlation
home_ownership is highly correlated with delinq_amnt and 8 other fieldsHigh correlation
chargeoff_within_12_mths is highly correlated with delinq_amnt and 20 other fieldsHigh correlation
loan_status is highly correlated with delinq_amnt and 9 other fieldsHigh correlation
tax_liens is highly correlated with delinq_amnt and 20 other fieldsHigh correlation
pub_rec_bankruptcies is highly correlated with delinq_amnt and 8 other fieldsHigh correlation
emp_title has 2459 (6.2%) missing values Missing
emp_length has 1075 (2.7%) missing values Missing
desc has 12940 (32.6%) missing values Missing
mths_since_last_delinq has 25682 (64.7%) missing values Missing
mths_since_last_record has 36931 (93.0%) missing values Missing
next_pymnt_d has 38577 (97.1%) missing values Missing
mths_since_last_major_derog has 39717 (100.0%) missing values Missing
annual_inc_joint has 39717 (100.0%) missing values Missing
dti_joint has 39717 (100.0%) missing values Missing
verification_status_joint has 39717 (100.0%) missing values Missing
tot_coll_amt has 39717 (100.0%) missing values Missing
tot_cur_bal has 39717 (100.0%) missing values Missing
open_acc_6m has 39717 (100.0%) missing values Missing
open_il_6m has 39717 (100.0%) missing values Missing
open_il_12m has 39717 (100.0%) missing values Missing
open_il_24m has 39717 (100.0%) missing values Missing
mths_since_rcnt_il has 39717 (100.0%) missing values Missing
total_bal_il has 39717 (100.0%) missing values Missing
il_util has 39717 (100.0%) missing values Missing
open_rv_12m has 39717 (100.0%) missing values Missing
open_rv_24m has 39717 (100.0%) missing values Missing
max_bal_bc has 39717 (100.0%) missing values Missing
all_util has 39717 (100.0%) missing values Missing
total_rev_hi_lim has 39717 (100.0%) missing values Missing
inq_fi has 39717 (100.0%) missing values Missing
total_cu_tl has 39717 (100.0%) missing values Missing
inq_last_12m has 39717 (100.0%) missing values Missing
acc_open_past_24mths has 39717 (100.0%) missing values Missing
avg_cur_bal has 39717 (100.0%) missing values Missing
bc_open_to_buy has 39717 (100.0%) missing values Missing
bc_util has 39717 (100.0%) missing values Missing
mo_sin_old_il_acct has 39717 (100.0%) missing values Missing
mo_sin_old_rev_tl_op has 39717 (100.0%) missing values Missing
mo_sin_rcnt_rev_tl_op has 39717 (100.0%) missing values Missing
mo_sin_rcnt_tl has 39717 (100.0%) missing values Missing
mort_acc has 39717 (100.0%) missing values Missing
mths_since_recent_bc has 39717 (100.0%) missing values Missing
mths_since_recent_bc_dlq has 39717 (100.0%) missing values Missing
mths_since_recent_inq has 39717 (100.0%) missing values Missing
mths_since_recent_revol_delinq has 39717 (100.0%) missing values Missing
num_accts_ever_120_pd has 39717 (100.0%) missing values Missing
num_actv_bc_tl has 39717 (100.0%) missing values Missing
num_actv_rev_tl has 39717 (100.0%) missing values Missing
num_bc_sats has 39717 (100.0%) missing values Missing
num_bc_tl has 39717 (100.0%) missing values Missing
num_il_tl has 39717 (100.0%) missing values Missing
num_op_rev_tl has 39717 (100.0%) missing values Missing
num_rev_accts has 39717 (100.0%) missing values Missing
num_rev_tl_bal_gt_0 has 39717 (100.0%) missing values Missing
num_sats has 39717 (100.0%) missing values Missing
num_tl_120dpd_2m has 39717 (100.0%) missing values Missing
num_tl_30dpd has 39717 (100.0%) missing values Missing
num_tl_90g_dpd_24m has 39717 (100.0%) missing values Missing
num_tl_op_past_12m has 39717 (100.0%) missing values Missing
pct_tl_nvr_dlq has 39717 (100.0%) missing values Missing
percent_bc_gt_75 has 39717 (100.0%) missing values Missing
pub_rec_bankruptcies has 697 (1.8%) missing values Missing
tot_hi_cred_lim has 39717 (100.0%) missing values Missing
total_bal_ex_mort has 39717 (100.0%) missing values Missing
total_bc_limit has 39717 (100.0%) missing values Missing
total_il_high_credit_limit has 39717 (100.0%) missing values Missing
annual_inc is highly skewed (γ1 = 30.9491846) Skewed
collection_recovery_fee is highly skewed (γ1 = 25.02941842) Skewed
url is uniformly distributed Uniform
id has unique values Unique
member_id has unique values Unique
url has unique values Unique
mths_since_last_major_derog is an unsupported type, check if it needs cleaning or further analysis Unsupported
annual_inc_joint is an unsupported type, check if it needs cleaning or further analysis Unsupported
dti_joint is an unsupported type, check if it needs cleaning or further analysis Unsupported
verification_status_joint is an unsupported type, check if it needs cleaning or further analysis Unsupported
tot_coll_amt is an unsupported type, check if it needs cleaning or further analysis Unsupported
tot_cur_bal is an unsupported type, check if it needs cleaning or further analysis Unsupported
open_acc_6m is an unsupported type, check if it needs cleaning or further analysis Unsupported
open_il_6m is an unsupported type, check if it needs cleaning or further analysis Unsupported
open_il_12m is an unsupported type, check if it needs cleaning or further analysis Unsupported
open_il_24m is an unsupported type, check if it needs cleaning or further analysis Unsupported
mths_since_rcnt_il is an unsupported type, check if it needs cleaning or further analysis Unsupported
total_bal_il is an unsupported type, check if it needs cleaning or further analysis Unsupported
il_util is an unsupported type, check if it needs cleaning or further analysis Unsupported
open_rv_12m is an unsupported type, check if it needs cleaning or further analysis Unsupported
open_rv_24m is an unsupported type, check if it needs cleaning or further analysis Unsupported
max_bal_bc is an unsupported type, check if it needs cleaning or further analysis Unsupported
all_util is an unsupported type, check if it needs cleaning or further analysis Unsupported
total_rev_hi_lim is an unsupported type, check if it needs cleaning or further analysis Unsupported
inq_fi is an unsupported type, check if it needs cleaning or further analysis Unsupported
total_cu_tl is an unsupported type, check if it needs cleaning or further analysis Unsupported
inq_last_12m is an unsupported type, check if it needs cleaning or further analysis Unsupported
acc_open_past_24mths is an unsupported type, check if it needs cleaning or further analysis Unsupported
avg_cur_bal is an unsupported type, check if it needs cleaning or further analysis Unsupported
bc_open_to_buy is an unsupported type, check if it needs cleaning or further analysis Unsupported
bc_util is an unsupported type, check if it needs cleaning or further analysis Unsupported
mo_sin_old_il_acct is an unsupported type, check if it needs cleaning or further analysis Unsupported
mo_sin_old_rev_tl_op is an unsupported type, check if it needs cleaning or further analysis Unsupported
mo_sin_rcnt_rev_tl_op is an unsupported type, check if it needs cleaning or further analysis Unsupported
mo_sin_rcnt_tl is an unsupported type, check if it needs cleaning or further analysis Unsupported
mort_acc is an unsupported type, check if it needs cleaning or further analysis Unsupported
mths_since_recent_bc is an unsupported type, check if it needs cleaning or further analysis Unsupported
mths_since_recent_bc_dlq is an unsupported type, check if it needs cleaning or further analysis Unsupported
mths_since_recent_inq is an unsupported type, check if it needs cleaning or further analysis Unsupported
mths_since_recent_revol_delinq is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_accts_ever_120_pd is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_actv_bc_tl is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_actv_rev_tl is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_bc_sats is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_bc_tl is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_il_tl is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_op_rev_tl is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_rev_accts is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_rev_tl_bal_gt_0 is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_sats is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_tl_120dpd_2m is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_tl_30dpd is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_tl_90g_dpd_24m is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_tl_op_past_12m is an unsupported type, check if it needs cleaning or further analysis Unsupported
pct_tl_nvr_dlq is an unsupported type, check if it needs cleaning or further analysis Unsupported
percent_bc_gt_75 is an unsupported type, check if it needs cleaning or further analysis Unsupported
tot_hi_cred_lim is an unsupported type, check if it needs cleaning or further analysis Unsupported
total_bal_ex_mort is an unsupported type, check if it needs cleaning or further analysis Unsupported
total_bc_limit is an unsupported type, check if it needs cleaning or further analysis Unsupported
total_il_high_credit_limit is an unsupported type, check if it needs cleaning or further analysis Unsupported
delinq_2yrs has 35405 (89.1%) zeros Zeros
inq_last_6mths has 19300 (48.6%) zeros Zeros
mths_since_last_delinq has 443 (1.1%) zeros Zeros
mths_since_last_record has 670 (1.7%) zeros Zeros
revol_bal has 994 (2.5%) zeros Zeros
out_prncp has 38577 (97.1%) zeros Zeros
out_prncp_inv has 38577 (97.1%) zeros Zeros
total_rec_late_fee has 37671 (94.8%) zeros Zeros
recoveries has 35499 (89.4%) zeros Zeros
collection_recovery_fee has 35935 (90.5%) zeros Zeros

Reproduction

Analysis started2021-04-14 05:47:44.569383
Analysis finished2021-04-14 05:51:04.566598
Duration3 minutes and 20 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct39717
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean683131.9131
Minimum54734
Maximum1077501
Zeros0
Zeros (%)0.0%
Memory size310.4 KiB
2021-04-14T11:21:04.737388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum54734
5-th percentile372418.4
Q1516221
median665665
Q3837755
95-th percentile1039966.2
Maximum1077501
Range1022767
Interquartile range (IQR)321534

Descriptive statistics

Standard deviation210694.1329
Coefficient of variation (CV)0.3084237889
Kurtosis-0.7298893992
Mean683131.9131
Median Absolute Deviation (MAD)160026
Skewness0.07880763188
Sum2.713195019 × 1010
Variance4.439201764 × 1010
MonotocityNot monotonic
2021-04-14T11:21:04.949113image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4648991
 
< 0.1%
8834181
 
< 0.1%
9673731
 
< 0.1%
8424461
 
< 0.1%
6437911
 
< 0.1%
6007841
 
< 0.1%
5163131
 
< 0.1%
8649791
 
< 0.1%
5751751
 
< 0.1%
3887741
 
< 0.1%
Other values (39707)39707
> 99.9%
ValueCountFrequency (%)
547341
< 0.1%
557421
< 0.1%
572451
< 0.1%
574161
< 0.1%
589151
< 0.1%
ValueCountFrequency (%)
10775011
< 0.1%
10774301
< 0.1%
10771751
< 0.1%
10768631
< 0.1%
10753581
< 0.1%

member_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct39717
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean850463.5594
Minimum70699
Maximum1314167
Zeros0
Zeros (%)0.0%
Memory size310.4 KiB
2021-04-14T11:21:05.174106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum70699
5-th percentile388192.4
Q1666780
median850812
Q31047339
95-th percentile1269461.8
Maximum1314167
Range1243468
Interquartile range (IQR)380559

Descriptive statistics

Standard deviation265678.3074
Coefficient of variation (CV)0.3123923471
Kurtosis-0.5629680132
Mean850463.5594
Median Absolute Deviation (MAD)190427
Skewness-0.2124163719
Sum3.377786119 × 1010
Variance7.058496303 × 1010
MonotocityNot monotonic
2021-04-14T11:21:05.481619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3996491
 
< 0.1%
8262871
 
< 0.1%
8035171
 
< 0.1%
8751981
 
< 0.1%
10697591
 
< 0.1%
6970251
 
< 0.1%
12254111
 
< 0.1%
12786611
 
< 0.1%
6929351
 
< 0.1%
5229541
 
< 0.1%
Other values (39707)39707
> 99.9%
ValueCountFrequency (%)
706991
< 0.1%
736731
< 0.1%
747241
< 0.1%
765831
< 0.1%
803531
< 0.1%
ValueCountFrequency (%)
13141671
< 0.1%
13135241
< 0.1%
13117481
< 0.1%
13114411
< 0.1%
13069571
< 0.1%

loan_amnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct885
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11219.44381
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Memory size310.4 KiB
2021-04-14T11:21:05.761514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15500
median10000
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9500

Descriptive statistics

Standard deviation7456.670694
Coefficient of variation (CV)0.6646203517
Kurtosis0.7686685518
Mean11219.44381
Median Absolute Deviation (MAD)5000
Skewness1.05931729
Sum445602650
Variance55601937.84
MonotocityNot monotonic
2021-04-14T11:21:06.068768image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100002833
 
7.1%
120002334
 
5.9%
50002051
 
5.2%
60001908
 
4.8%
150001895
 
4.8%
200001626
 
4.1%
80001586
 
4.0%
250001390
 
3.5%
40001130
 
2.8%
30001030
 
2.6%
Other values (875)21934
55.2%
ValueCountFrequency (%)
5005
< 0.1%
7001
 
< 0.1%
7251
 
< 0.1%
7501
 
< 0.1%
8001
 
< 0.1%
ValueCountFrequency (%)
35000679
1.7%
348002
 
< 0.1%
346751
 
< 0.1%
345251
 
< 0.1%
344755
 
< 0.1%

funded_amnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1041
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10947.7132
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Memory size310.4 KiB
2021-04-14T11:21:06.366658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15400
median9600
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9600

Descriptive statistics

Standard deviation7187.23867
Coefficient of variation (CV)0.6565059334
Kurtosis0.9375519943
Mean10947.7132
Median Absolute Deviation (MAD)4600
Skewness1.081710238
Sum434810325
Variance51656399.7
MonotocityNot monotonic
2021-04-14T11:21:06.634961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100002741
 
6.9%
120002244
 
5.6%
50002040
 
5.1%
60001898
 
4.8%
150001784
 
4.5%
80001573
 
4.0%
200001456
 
3.7%
250001133
 
2.9%
40001127
 
2.8%
30001022
 
2.6%
Other values (1031)22699
57.2%
ValueCountFrequency (%)
5005
< 0.1%
7001
 
< 0.1%
7251
 
< 0.1%
7501
 
< 0.1%
8001
 
< 0.1%
ValueCountFrequency (%)
35000554
1.4%
348001
 
< 0.1%
346752
 
< 0.1%
345251
 
< 0.1%
344754
 
< 0.1%

funded_amnt_inv
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8205
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10397.44887
Minimum0
Maximum35000
Zeros129
Zeros (%)0.3%
Memory size310.4 KiB
2021-04-14T11:21:06.885587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1873.658
Q15000
median8975
Q314400
95-th percentile24736.57226
Maximum35000
Range35000
Interquartile range (IQR)9400

Descriptive statistics

Standard deviation7128.450439
Coefficient of variation (CV)0.6855961044
Kurtosis1.062544362
Mean10397.44887
Median Absolute Deviation (MAD)4200
Skewness1.106212938
Sum412955476.7
Variance50814805.66
MonotocityNot monotonic
2021-04-14T11:21:07.094944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50001309
 
3.3%
100001275
 
3.2%
60001200
 
3.0%
120001069
 
2.7%
8000900
 
2.3%
4000812
 
2.0%
3000803
 
2.0%
15000657
 
1.7%
7000600
 
1.5%
2000452
 
1.1%
Other values (8195)30640
77.1%
ValueCountFrequency (%)
0129
0.3%
0.0001210981
 
< 0.1%
0.0005311331
 
< 0.1%
0.0006546071
 
< 0.1%
0.0018676961
 
< 0.1%
ValueCountFrequency (%)
35000135
0.3%
34997.352451
 
< 0.1%
34993.655391
 
< 0.1%
34993.325711
 
< 0.1%
34993.263061
 
< 0.1%

term
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
36 months
29096 
60 months
10621 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters397170
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 60 months
3rd row 36 months
4th row 36 months
5th row 60 months
ValueCountFrequency (%)
36 months29096
73.3%
60 months10621
 
26.7%
2021-04-14T11:21:07.463362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:21:07.571689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
months39717
50.0%
3629096
36.6%
6010621
 
13.4%

Most occurring characters

ValueCountFrequency (%)
79434
20.0%
639717
10.0%
m39717
10.0%
o39717
10.0%
n39717
10.0%
t39717
10.0%
h39717
10.0%
s39717
10.0%
329096
 
7.3%
010621
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter238302
60.0%
Space Separator79434
 
20.0%
Decimal Number79434
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
m39717
16.7%
o39717
16.7%
n39717
16.7%
t39717
16.7%
h39717
16.7%
s39717
16.7%
ValueCountFrequency (%)
639717
50.0%
329096
36.6%
010621
 
13.4%
ValueCountFrequency (%)
79434
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin238302
60.0%
Common158868
40.0%

Most frequent character per script

ValueCountFrequency (%)
m39717
16.7%
o39717
16.7%
n39717
16.7%
t39717
16.7%
h39717
16.7%
s39717
16.7%
ValueCountFrequency (%)
79434
50.0%
639717
25.0%
329096
 
18.3%
010621
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII397170
100.0%

Most frequent character per block

ValueCountFrequency (%)
79434
20.0%
639717
10.0%
m39717
10.0%
o39717
10.0%
n39717
10.0%
t39717
10.0%
h39717
10.0%
s39717
10.0%
329096
 
7.3%
010621
 
2.7%

int_rate
Categorical

HIGH CARDINALITY

Distinct371
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
10.99%
 
956
13.49%
 
826
11.49%
 
825
7.51%
 
787
7.88%
 
725
Other values (366)
35598 

Length

Max length6
Median length6
Mean length5.694287081
Min length5

Characters and Unicode

Total characters226160
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st row10.65%
2nd row15.27%
3rd row15.96%
4th row13.49%
5th row12.69%
ValueCountFrequency (%)
10.99%956
 
2.4%
13.49%826
 
2.1%
11.49%825
 
2.1%
7.51%787
 
2.0%
7.88%725
 
1.8%
7.49%656
 
1.7%
11.71%607
 
1.5%
9.99%603
 
1.5%
7.90%582
 
1.5%
5.42%573
 
1.4%
Other values (361)32577
82.0%
2021-04-14T11:21:07.980062image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10.99956
 
2.4%
13.49826
 
2.1%
11.49825
 
2.1%
7.51787
 
2.0%
7.88725
 
1.8%
7.49656
 
1.7%
11.71607
 
1.5%
9.99603
 
1.5%
7.90582
 
1.5%
5.42573
 
1.4%
Other values (361)32577
82.0%

Most occurring characters

ValueCountFrequency (%)
.39717
17.6%
%39717
17.6%
138195
16.9%
921893
9.7%
212734
 
5.6%
712132
 
5.4%
612033
 
5.3%
411091
 
4.9%
59947
 
4.4%
39929
 
4.4%
Other values (2)18772
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number146726
64.9%
Other Punctuation79434
35.1%

Most frequent character per category

ValueCountFrequency (%)
138195
26.0%
921893
14.9%
212734
 
8.7%
712132
 
8.3%
612033
 
8.2%
411091
 
7.6%
59947
 
6.8%
39929
 
6.8%
89527
 
6.5%
09245
 
6.3%
ValueCountFrequency (%)
.39717
50.0%
%39717
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common226160
100.0%

Most frequent character per script

ValueCountFrequency (%)
.39717
17.6%
%39717
17.6%
138195
16.9%
921893
9.7%
212734
 
5.6%
712132
 
5.4%
612033
 
5.3%
411091
 
4.9%
59947
 
4.4%
39929
 
4.4%
Other values (2)18772
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII226160
100.0%

Most frequent character per block

ValueCountFrequency (%)
.39717
17.6%
%39717
17.6%
138195
16.9%
921893
9.7%
212734
 
5.6%
712132
 
5.4%
612033
 
5.3%
411091
 
4.9%
59947
 
4.4%
39929
 
4.4%
Other values (2)18772
8.3%

installment
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15383
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324.5619221
Minimum15.69
Maximum1305.19
Zeros0
Zeros (%)0.0%
Memory size310.4 KiB
2021-04-14T11:21:08.168967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum15.69
5-th percentile71.246
Q1167.02
median280.22
Q3430.78
95-th percentile762.996
Maximum1305.19
Range1289.5
Interquartile range (IQR)263.76

Descriptive statistics

Standard deviation208.8748735
Coefficient of variation (CV)0.6435593929
Kurtosis1.246801303
Mean324.5619221
Median Absolute Deviation (MAD)123.2
Skewness1.128419095
Sum12890625.86
Variance43628.71279
MonotocityNot monotonic
2021-04-14T11:21:08.372343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311.1168
 
0.2%
180.9659
 
0.1%
311.0254
 
0.1%
150.848
 
0.1%
368.4546
 
0.1%
372.1245
 
0.1%
330.7643
 
0.1%
339.3142
 
0.1%
301.641
 
0.1%
317.7241
 
0.1%
Other values (15373)39230
98.8%
ValueCountFrequency (%)
15.691
< 0.1%
16.081
< 0.1%
16.251
< 0.1%
16.311
< 0.1%
16.471
< 0.1%
ValueCountFrequency (%)
1305.191
< 0.1%
1302.691
< 0.1%
1295.211
< 0.1%
1288.12
< 0.1%
1283.51
< 0.1%

grade
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
B
12020 
A
10085 
C
8098 
D
5307 
E
2842 
Other values (2)
1365 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39717
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC
3rd rowC
4th rowC
5th rowB
ValueCountFrequency (%)
B12020
30.3%
A10085
25.4%
C8098
20.4%
D5307
13.4%
E2842
 
7.2%
F1049
 
2.6%
G316
 
0.8%
2021-04-14T11:21:08.743004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:21:08.866107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
b12020
30.3%
a10085
25.4%
c8098
20.4%
d5307
13.4%
e2842
 
7.2%
f1049
 
2.6%
g316
 
0.8%

Most occurring characters

ValueCountFrequency (%)
B12020
30.3%
A10085
25.4%
C8098
20.4%
D5307
13.4%
E2842
 
7.2%
F1049
 
2.6%
G316
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter39717
100.0%

Most frequent character per category

ValueCountFrequency (%)
B12020
30.3%
A10085
25.4%
C8098
20.4%
D5307
13.4%
E2842
 
7.2%
F1049
 
2.6%
G316
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin39717
100.0%

Most frequent character per script

ValueCountFrequency (%)
B12020
30.3%
A10085
25.4%
C8098
20.4%
D5307
13.4%
E2842
 
7.2%
F1049
 
2.6%
G316
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII39717
100.0%

Most frequent character per block

ValueCountFrequency (%)
B12020
30.3%
A10085
25.4%
C8098
20.4%
D5307
13.4%
E2842
 
7.2%
F1049
 
2.6%
G316
 
0.8%

sub_grade
Categorical

HIGH CORRELATION

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
B3
2917 
A4
2886 
A5
2742 
B5
2704 
B4
 
2512
Other values (30)
25956 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters79434
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB2
2nd rowC4
3rd rowC5
4th rowC1
5th rowB5
ValueCountFrequency (%)
B32917
 
7.3%
A42886
 
7.3%
A52742
 
6.9%
B52704
 
6.8%
B42512
 
6.3%
C12136
 
5.4%
B22057
 
5.2%
C22011
 
5.1%
B11830
 
4.6%
A31810
 
4.6%
Other values (25)16112
40.6%
2021-04-14T11:21:09.320858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b32917
 
7.3%
a42886
 
7.3%
a52742
 
6.9%
b52704
 
6.8%
b42512
 
6.3%
c12136
 
5.4%
b22057
 
5.2%
c22011
 
5.1%
b11830
 
4.6%
a31810
 
4.6%
Other values (25)16112
40.6%

Most occurring characters

ValueCountFrequency (%)
B12020
15.1%
A10085
12.7%
48293
10.4%
38215
10.3%
C8098
10.2%
58070
10.2%
27907
10.0%
17232
9.1%
D5307
6.7%
E2842
 
3.6%
Other values (2)1365
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter39717
50.0%
Decimal Number39717
50.0%

Most frequent character per category

ValueCountFrequency (%)
B12020
30.3%
A10085
25.4%
C8098
20.4%
D5307
13.4%
E2842
 
7.2%
F1049
 
2.6%
G316
 
0.8%
ValueCountFrequency (%)
48293
20.9%
38215
20.7%
58070
20.3%
27907
19.9%
17232
18.2%

Most occurring scripts

ValueCountFrequency (%)
Latin39717
50.0%
Common39717
50.0%

Most frequent character per script

ValueCountFrequency (%)
B12020
30.3%
A10085
25.4%
C8098
20.4%
D5307
13.4%
E2842
 
7.2%
F1049
 
2.6%
G316
 
0.8%
ValueCountFrequency (%)
48293
20.9%
38215
20.7%
58070
20.3%
27907
19.9%
17232
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII79434
100.0%

Most frequent character per block

ValueCountFrequency (%)
B12020
15.1%
A10085
12.7%
48293
10.4%
38215
10.3%
C8098
10.2%
58070
10.2%
27907
10.0%
17232
9.1%
D5307
6.7%
E2842
 
3.6%
Other values (2)1365
 
1.7%

emp_title
Categorical

HIGH CARDINALITY
MISSING

Distinct28820
Distinct (%)77.4%
Missing2459
Missing (%)6.2%
Memory size310.4 KiB
US Army
 
134
Bank of America
 
109
IBM
 
66
AT&T
 
59
Kaiser Permanente
 
56
Other values (28815)
36834 

Length

Max length78
Median length18
Mean length18.37978421
Min length2

Characters and Unicode

Total characters684794
Distinct characters96
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25641 ?
Unique (%)68.8%

Sample

1st rowRyder
2nd rowAIR RESOURCES BOARD
3rd rowUniversity Medical Group
4th rowVeolia Transportaton
5th rowSouthern Star Photography
ValueCountFrequency (%)
US Army134
 
0.3%
Bank of America109
 
0.3%
IBM66
 
0.2%
AT&T59
 
0.1%
Kaiser Permanente56
 
0.1%
Wells Fargo54
 
0.1%
USAF54
 
0.1%
UPS53
 
0.1%
US Air Force52
 
0.1%
Walmart45
 
0.1%
Other values (28810)36576
92.1%
(Missing)2459
 
6.2%
2021-04-14T11:21:10.469498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc3197
 
3.2%
of3008
 
3.0%
1208
 
1.2%
and963
 
1.0%
center818
 
0.8%
bank805
 
0.8%
county803
 
0.8%
services795
 
0.8%
school750
 
0.7%
the747
 
0.7%
Other values (18882)87491
87.0%

Most occurring characters

ValueCountFrequency (%)
64766
 
9.5%
e55954
 
8.2%
a43836
 
6.4%
n42641
 
6.2%
o42586
 
6.2%
i40491
 
5.9%
r40067
 
5.9%
t38580
 
5.6%
s30254
 
4.4%
l25923
 
3.8%
Other values (86)259696
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter489338
71.5%
Uppercase Letter119545
 
17.5%
Space Separator64766
 
9.5%
Other Punctuation8798
 
1.3%
Dash Punctuation1031
 
0.2%
Decimal Number968
 
0.1%
Open Punctuation159
 
< 0.1%
Close Punctuation156
 
< 0.1%
Math Symbol21
 
< 0.1%
Other Symbol2
 
< 0.1%
Other values (5)10
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
C14579
 
12.2%
S13325
 
11.1%
A8885
 
7.4%
I7566
 
6.3%
M6518
 
5.5%
P6077
 
5.1%
T5691
 
4.8%
L5561
 
4.7%
E5241
 
4.4%
D5056
 
4.2%
Other values (18)41046
34.3%
ValueCountFrequency (%)
e55954
11.4%
a43836
9.0%
n42641
8.7%
o42586
8.7%
i40491
 
8.3%
r40067
 
8.2%
t38580
 
7.9%
s30254
 
6.2%
l25923
 
5.3%
c23099
 
4.7%
Other values (17)105907
21.6%
ValueCountFrequency (%)
.4253
48.3%
,2194
24.9%
&1301
 
14.8%
'652
 
7.4%
/311
 
3.5%
#36
 
0.4%
@10
 
0.1%
:9
 
0.1%
"8
 
0.1%
!8
 
0.1%
Other values (5)16
 
0.2%
ValueCountFrequency (%)
1192
19.8%
2161
16.6%
3155
16.0%
098
10.1%
491
9.4%
572
 
7.4%
962
 
6.4%
658
 
6.0%
746
 
4.8%
833
 
3.4%
ValueCountFrequency (%)
+18
85.7%
|2
 
9.5%
<1
 
4.8%
ValueCountFrequency (%)
(158
99.4%
[1
 
0.6%
ValueCountFrequency (%)
€1
50.0%
ƒ1
50.0%
ValueCountFrequency (%)
¢1
50.0%
$1
50.0%
ValueCountFrequency (%)
64766
100.0%
ValueCountFrequency (%)
-1031
100.0%
ValueCountFrequency (%)
)156
100.0%
ValueCountFrequency (%)
©2
100.0%
ValueCountFrequency (%)
`2
100.0%
ValueCountFrequency (%)
_2
100.0%
ValueCountFrequency (%)
²2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin608883
88.9%
Common75911
 
11.1%

Most frequent character per script

ValueCountFrequency (%)
e55954
 
9.2%
a43836
 
7.2%
n42641
 
7.0%
o42586
 
7.0%
i40491
 
6.7%
r40067
 
6.6%
t38580
 
6.3%
s30254
 
5.0%
l25923
 
4.3%
c23099
 
3.8%
Other values (45)225452
37.0%
ValueCountFrequency (%)
64766
85.3%
.4253
 
5.6%
,2194
 
2.9%
&1301
 
1.7%
-1031
 
1.4%
'652
 
0.9%
/311
 
0.4%
1192
 
0.3%
2161
 
0.2%
(158
 
0.2%
Other values (31)892
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII684780
> 99.9%
None14
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
64766
 
9.5%
e55954
 
8.2%
a43836
 
6.4%
n42641
 
6.2%
o42586
 
6.2%
i40491
 
5.9%
r40067
 
5.9%
t38580
 
5.6%
s30254
 
4.4%
l25923
 
3.8%
Other values (77)259682
37.9%
ValueCountFrequency (%)
Ã3
21.4%
©2
14.3%
Â2
14.3%
²2
14.3%
â1
 
7.1%
€1
 
7.1%
¢1
 
7.1%
ƒ1
 
7.1%
¡1
 
7.1%

emp_length
Categorical

HIGH CORRELATION
MISSING

Distinct11
Distinct (%)< 0.1%
Missing1075
Missing (%)2.7%
Memory size310.4 KiB
10+ years
8879 
< 1 year
4583 
2 years
4388 
3 years
4095 
4 years
3436 
Other values (6)
13261 

Length

Max length9
Median length7
Mean length7.494306713
Min length6

Characters and Unicode

Total characters289595
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10+ years
2nd row< 1 year
3rd row10+ years
4th row10+ years
5th row1 year
ValueCountFrequency (%)
10+ years8879
22.4%
< 1 year4583
11.5%
2 years4388
11.0%
3 years4095
10.3%
4 years3436
 
8.7%
5 years3282
 
8.3%
1 year3240
 
8.2%
6 years2229
 
5.6%
7 years1773
 
4.5%
8 years1479
 
3.7%
2021-04-14T11:21:11.418265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years30819
37.6%
108879
 
10.8%
17823
 
9.6%
year7823
 
9.6%
4583
 
5.6%
24388
 
5.4%
34095
 
5.0%
43436
 
4.2%
53282
 
4.0%
62229
 
2.7%
Other values (3)4510
 
5.5%

Most occurring characters

ValueCountFrequency (%)
43225
14.9%
y38642
13.3%
e38642
13.3%
a38642
13.3%
r38642
13.3%
s30819
10.6%
116702
 
5.8%
08879
 
3.1%
+8879
 
3.1%
<4583
 
1.6%
Other values (8)21940
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter185387
64.0%
Decimal Number47521
 
16.4%
Space Separator43225
 
14.9%
Math Symbol13462
 
4.6%

Most frequent character per category

ValueCountFrequency (%)
116702
35.1%
08879
18.7%
24388
 
9.2%
34095
 
8.6%
43436
 
7.2%
53282
 
6.9%
62229
 
4.7%
71773
 
3.7%
81479
 
3.1%
91258
 
2.6%
ValueCountFrequency (%)
y38642
20.8%
e38642
20.8%
a38642
20.8%
r38642
20.8%
s30819
16.6%
ValueCountFrequency (%)
+8879
66.0%
<4583
34.0%
ValueCountFrequency (%)
43225
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin185387
64.0%
Common104208
36.0%

Most frequent character per script

ValueCountFrequency (%)
43225
41.5%
116702
 
16.0%
08879
 
8.5%
+8879
 
8.5%
<4583
 
4.4%
24388
 
4.2%
34095
 
3.9%
43436
 
3.3%
53282
 
3.1%
62229
 
2.1%
Other values (3)4510
 
4.3%
ValueCountFrequency (%)
y38642
20.8%
e38642
20.8%
a38642
20.8%
r38642
20.8%
s30819
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII289595
100.0%

Most frequent character per block

ValueCountFrequency (%)
43225
14.9%
y38642
13.3%
e38642
13.3%
a38642
13.3%
r38642
13.3%
s30819
10.6%
116702
 
5.8%
08879
 
3.1%
+8879
 
3.1%
<4583
 
1.6%
Other values (8)21940
7.6%

home_ownership
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
RENT
18899 
MORTGAGE
17659 
OWN
3058 
OTHER
 
98
NONE
 
3

Length

Max length8
Median length4
Mean length5.703955485
Min length3

Characters and Unicode

Total characters226544
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowRENT
4th rowRENT
5th rowRENT
ValueCountFrequency (%)
RENT18899
47.6%
MORTGAGE17659
44.5%
OWN3058
 
7.7%
OTHER98
 
0.2%
NONE3
 
< 0.1%
2021-04-14T11:21:11.966833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:21:12.105374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
rent18899
47.6%
mortgage17659
44.5%
own3058
 
7.7%
other98
 
0.2%
none3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E36659
16.2%
R36656
16.2%
T36656
16.2%
G35318
15.6%
N21963
9.7%
O20818
9.2%
M17659
7.8%
A17659
7.8%
W3058
 
1.3%
H98
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter226544
100.0%

Most frequent character per category

ValueCountFrequency (%)
E36659
16.2%
R36656
16.2%
T36656
16.2%
G35318
15.6%
N21963
9.7%
O20818
9.2%
M17659
7.8%
A17659
7.8%
W3058
 
1.3%
H98
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin226544
100.0%

Most frequent character per script

ValueCountFrequency (%)
E36659
16.2%
R36656
16.2%
T36656
16.2%
G35318
15.6%
N21963
9.7%
O20818
9.2%
M17659
7.8%
A17659
7.8%
W3058
 
1.3%
H98
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII226544
100.0%

Most frequent character per block

ValueCountFrequency (%)
E36659
16.2%
R36656
16.2%
T36656
16.2%
G35318
15.6%
N21963
9.7%
O20818
9.2%
M17659
7.8%
A17659
7.8%
W3058
 
1.3%
H98
 
< 0.1%

annual_inc
Real number (ℝ≥0)

SKEWED

Distinct5318
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68968.92638
Minimum4000
Maximum6000000
Zeros0
Zeros (%)0.0%
Memory size310.4 KiB
2021-04-14T11:21:12.392705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile24000
Q140404
median59000
Q382300
95-th percentile142000
Maximum6000000
Range5996000
Interquartile range (IQR)41896

Descriptive statistics

Standard deviation63793.76579
Coefficient of variation (CV)0.9249638807
Kurtosis2302.737777
Mean68968.92638
Median Absolute Deviation (MAD)20000
Skewness30.9491846
Sum2739238849
Variance4069644554
MonotocityNot monotonic
2021-04-14T11:21:12.779044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600001505
 
3.8%
500001057
 
2.7%
40000876
 
2.2%
45000830
 
2.1%
30000825
 
2.1%
75000811
 
2.0%
65000803
 
2.0%
70000733
 
1.8%
48000723
 
1.8%
80000662
 
1.7%
Other values (5308)30892
77.8%
ValueCountFrequency (%)
40001
 
< 0.1%
40801
 
< 0.1%
42002
< 0.1%
48004
< 0.1%
48881
 
< 0.1%
ValueCountFrequency (%)
60000001
< 0.1%
39000001
< 0.1%
20397841
< 0.1%
19000001
< 0.1%
17820001
< 0.1%

verification_status
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
Not Verified
16921 
Verified
12809 
Source Verified
9987 

Length

Max length15
Median length12
Mean length11.46433517
Min length8

Characters and Unicode

Total characters455329
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVerified
2nd rowSource Verified
3rd rowNot Verified
4th rowSource Verified
5th rowSource Verified
ValueCountFrequency (%)
Not Verified16921
42.6%
Verified12809
32.3%
Source Verified9987
25.1%
2021-04-14T11:21:13.392945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:21:13.554380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
verified39717
59.6%
not16921
25.4%
source9987
 
15.0%

Most occurring characters

ValueCountFrequency (%)
e89421
19.6%
i79434
17.4%
r49704
10.9%
V39717
8.7%
f39717
8.7%
d39717
8.7%
o26908
 
5.9%
26908
 
5.9%
N16921
 
3.7%
t16921
 
3.7%
Other values (3)29961
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter361796
79.5%
Uppercase Letter66625
 
14.6%
Space Separator26908
 
5.9%

Most frequent character per category

ValueCountFrequency (%)
e89421
24.7%
i79434
22.0%
r49704
13.7%
f39717
11.0%
d39717
11.0%
o26908
 
7.4%
t16921
 
4.7%
u9987
 
2.8%
c9987
 
2.8%
ValueCountFrequency (%)
V39717
59.6%
N16921
25.4%
S9987
 
15.0%
ValueCountFrequency (%)
26908
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin428421
94.1%
Common26908
 
5.9%

Most frequent character per script

ValueCountFrequency (%)
e89421
20.9%
i79434
18.5%
r49704
11.6%
V39717
9.3%
f39717
9.3%
d39717
9.3%
o26908
 
6.3%
N16921
 
3.9%
t16921
 
3.9%
S9987
 
2.3%
Other values (2)19974
 
4.7%
ValueCountFrequency (%)
26908
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII455329
100.0%

Most frequent character per block

ValueCountFrequency (%)
e89421
19.6%
i79434
17.4%
r49704
10.9%
V39717
8.7%
f39717
8.7%
d39717
8.7%
o26908
 
5.9%
26908
 
5.9%
N16921
 
3.7%
t16921
 
3.7%
Other values (3)29961
 
6.6%

issue_d
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
Dec-11
 
2260
Nov-11
 
2223
Oct-11
 
2114
Sep-11
 
2063
Aug-11
 
1928
Other values (50)
29129 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters238302
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDec-11
2nd rowDec-11
3rd rowDec-11
4th rowDec-11
5th rowDec-11
ValueCountFrequency (%)
Dec-112260
 
5.7%
Nov-112223
 
5.6%
Oct-112114
 
5.3%
Sep-112063
 
5.2%
Aug-111928
 
4.9%
Jul-111870
 
4.7%
Jun-111827
 
4.6%
May-111689
 
4.3%
Apr-111562
 
3.9%
Mar-111443
 
3.6%
Other values (45)20738
52.2%
2021-04-14T11:21:13.946440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dec-112260
 
5.7%
nov-112223
 
5.6%
oct-112114
 
5.3%
sep-112063
 
5.2%
aug-111928
 
4.9%
jul-111870
 
4.7%
jun-111827
 
4.6%
may-111689
 
4.3%
apr-111562
 
3.9%
mar-111443
 
3.6%
Other values (45)20738
52.2%

Most occurring characters

ValueCountFrequency (%)
154844
23.0%
-39717
16.7%
018061
 
7.6%
e10439
 
4.4%
u10273
 
4.3%
J9134
 
3.8%
c8367
 
3.5%
a8070
 
3.4%
p6482
 
2.7%
A6352
 
2.7%
Other values (18)66563
27.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter79434
33.3%
Decimal Number79434
33.3%
Uppercase Letter39717
16.7%
Dash Punctuation39717
16.7%

Most frequent character per category

ValueCountFrequency (%)
e10439
13.1%
u10273
12.9%
c8367
10.5%
a8070
10.2%
p6482
8.2%
n5658
7.1%
r5526
7.0%
o4167
 
5.2%
v4167
 
5.2%
t3934
 
5.0%
Other values (4)12351
15.5%
ValueCountFrequency (%)
J9134
23.0%
A6352
16.0%
M5691
14.3%
D4433
11.2%
N4167
10.5%
O3934
9.9%
S3648
 
9.2%
F2358
 
5.9%
ValueCountFrequency (%)
154844
69.0%
018061
 
22.7%
94716
 
5.9%
81562
 
2.0%
7251
 
0.3%
ValueCountFrequency (%)
-39717
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin119151
50.0%
Common119151
50.0%

Most frequent character per script

ValueCountFrequency (%)
e10439
 
8.8%
u10273
 
8.6%
J9134
 
7.7%
c8367
 
7.0%
a8070
 
6.8%
p6482
 
5.4%
A6352
 
5.3%
M5691
 
4.8%
n5658
 
4.7%
r5526
 
4.6%
Other values (12)43159
36.2%
ValueCountFrequency (%)
154844
46.0%
-39717
33.3%
018061
 
15.2%
94716
 
4.0%
81562
 
1.3%
7251
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII238302
100.0%

Most frequent character per block

ValueCountFrequency (%)
154844
23.0%
-39717
16.7%
018061
 
7.6%
e10439
 
4.4%
u10273
 
4.3%
J9134
 
3.8%
c8367
 
3.5%
a8070
 
3.4%
p6482
 
2.7%
A6352
 
2.7%
Other values (18)66563
27.9%

loan_status
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
Fully Paid
32950 
Charged Off
5627 
Current
 
1140

Length

Max length11
Median length10
Mean length10.05556814
Min length7

Characters and Unicode

Total characters399377
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully Paid
2nd rowCharged Off
3rd rowFully Paid
4th rowFully Paid
5th rowCurrent
ValueCountFrequency (%)
Fully Paid32950
83.0%
Charged Off5627
 
14.2%
Current1140
 
2.9%
2021-04-14T11:21:14.384470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:21:14.530911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
fully32950
42.1%
paid32950
42.1%
off5627
 
7.2%
charged5627
 
7.2%
current1140
 
1.5%

Most occurring characters

ValueCountFrequency (%)
l65900
16.5%
38577
9.7%
a38577
9.7%
d38577
9.7%
u34090
8.5%
F32950
8.3%
y32950
8.3%
P32950
8.3%
i32950
8.3%
f11254
 
2.8%
Other values (8)40602
10.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter282506
70.7%
Uppercase Letter78294
 
19.6%
Space Separator38577
 
9.7%

Most frequent character per category

ValueCountFrequency (%)
l65900
23.3%
a38577
13.7%
d38577
13.7%
u34090
12.1%
y32950
11.7%
i32950
11.7%
f11254
 
4.0%
r7907
 
2.8%
e6767
 
2.4%
h5627
 
2.0%
Other values (3)7907
 
2.8%
ValueCountFrequency (%)
F32950
42.1%
P32950
42.1%
C6767
 
8.6%
O5627
 
7.2%
ValueCountFrequency (%)
38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin360800
90.3%
Common38577
 
9.7%

Most frequent character per script

ValueCountFrequency (%)
l65900
18.3%
a38577
10.7%
d38577
10.7%
u34090
9.4%
F32950
9.1%
y32950
9.1%
P32950
9.1%
i32950
9.1%
f11254
 
3.1%
r7907
 
2.2%
Other values (7)32695
9.1%
ValueCountFrequency (%)
38577
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII399377
100.0%

Most frequent character per block

ValueCountFrequency (%)
l65900
16.5%
38577
9.7%
a38577
9.7%
d38577
9.7%
u34090
8.5%
F32950
8.3%
y32950
8.3%
P32950
8.3%
i32950
8.3%
f11254
 
2.8%
Other values (8)40602
10.2%

pymnt_plan
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
False
39717 
ValueCountFrequency (%)
False39717
100.0%
2021-04-14T11:21:14.617838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

url
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct39717
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
https://lendingclub.com/browse/loanDetail.action?loan_id=450292
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=665245
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=659209
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=180712
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=653306
 
1
Other values (39712)
39712 

Length

Max length64
Median length63
Mean length63.10836669
Min length62

Characters and Unicode

Total characters2506475
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39717 ?
Unique (%)100.0%

Sample

1st rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077501
2nd rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077430
3rd rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077175
4th rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1076863
5th rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1075358
ValueCountFrequency (%)
https://lendingclub.com/browse/loanDetail.action?loan_id=4502921
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=6652451
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=6592091
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=1807121
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=6533061
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=8311931
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=3564311
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=4631301
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=9892941
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=5439731
 
< 0.1%
Other values (39707)39707
> 99.9%
2021-04-14T11:21:15.241616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://lendingclub.com/browse/loandetail.action?loan_id=4798641
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=6570361
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=8856291
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=6528851
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=7194841
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=8655531
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=5357211
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=9704041
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=10652341
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=5410261
 
< 0.1%
Other values (39707)39707
> 99.9%

Most occurring characters

ValueCountFrequency (%)
l198585
 
7.9%
n198585
 
7.9%
o198585
 
7.9%
t158868
 
6.3%
/158868
 
6.3%
i158868
 
6.3%
a158868
 
6.3%
e119151
 
4.8%
c119151
 
4.8%
s79434
 
3.2%
Other values (25)957512
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1826982
72.9%
Other Punctuation317736
 
12.7%
Decimal Number242606
 
9.7%
Uppercase Letter39717
 
1.6%
Connector Punctuation39717
 
1.6%
Math Symbol39717
 
1.6%

Most frequent character per category

ValueCountFrequency (%)
l198585
10.9%
n198585
10.9%
o198585
10.9%
t158868
8.7%
i158868
8.7%
a158868
8.7%
e119151
 
6.5%
c119151
 
6.5%
s79434
 
4.3%
d79434
 
4.3%
Other values (8)357453
19.6%
ValueCountFrequency (%)
526616
11.0%
626607
11.0%
726037
10.7%
825774
10.6%
425584
10.5%
124160
10.0%
023856
9.8%
322052
9.1%
921694
8.9%
220226
8.3%
ValueCountFrequency (%)
/158868
50.0%
.79434
25.0%
:39717
 
12.5%
?39717
 
12.5%
ValueCountFrequency (%)
D39717
100.0%
ValueCountFrequency (%)
_39717
100.0%
ValueCountFrequency (%)
=39717
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1866699
74.5%
Common639776
 
25.5%

Most frequent character per script

ValueCountFrequency (%)
l198585
10.6%
n198585
10.6%
o198585
10.6%
t158868
 
8.5%
i158868
 
8.5%
a158868
 
8.5%
e119151
 
6.4%
c119151
 
6.4%
s79434
 
4.3%
d79434
 
4.3%
Other values (9)397170
21.3%
ValueCountFrequency (%)
/158868
24.8%
.79434
12.4%
:39717
 
6.2%
?39717
 
6.2%
_39717
 
6.2%
=39717
 
6.2%
526616
 
4.2%
626607
 
4.2%
726037
 
4.1%
825774
 
4.0%
Other values (6)137572
21.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2506475
100.0%

Most frequent character per block

ValueCountFrequency (%)
l198585
 
7.9%
n198585
 
7.9%
o198585
 
7.9%
t158868
 
6.3%
/158868
 
6.3%
i158868
 
6.3%
a158868
 
6.3%
e119151
 
4.8%
c119151
 
4.8%
s79434
 
3.2%
Other values (25)957512
38.2%

desc
Categorical

HIGH CARDINALITY
MISSING

Distinct26527
Distinct (%)99.1%
Missing12940
Missing (%)32.6%
Memory size310.4 KiB
 
210
Debt Consolidation
 
8
Camping Membership
 
6
credit card debt consolidation
 
3
personal loan
 
3
Other values (26522)
26547 

Length

Max length3988
Median length285
Mean length426.4940434
Min length1

Characters and Unicode

Total characters11420231
Distinct characters142
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26499 ?
Unique (%)99.0%

Sample

1st row Borrower added on 12/22/11 > I need to upgrade my business technologies.<br>
2nd row Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike. I only need this money because the deal im looking at is to good to pass up.<br><br> Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike.I only need this money because the deal im looking at is to good to pass up. I have finished college with an associates degree in business and its takingmeplaces<br>
3rd row Borrower added on 12/21/11 > to pay for property tax (borrow from friend, need to pay back) & central A/C need to be replace. I'm very sorry to let my loan expired last time.<br>
4th row Borrower added on 12/21/11 > I plan on combining three large interest bills together and freeing up some extra each month to pay toward other bills. I've always been a good payor but have found myself needing to make adjustments to my budget due to a medical scare. My job is very stable, I love it.<br>
5th row Borrower added on 12/18/11 > I am planning on using the funds to pay off two retail credit cards with 24.99% interest rates, as well as a major bank credit card with a 18.99% rate. I pay all my bills on time, looking for a lower combined payment and lower monthly payment.<br>
ValueCountFrequency (%)
210
 
0.5%
Debt Consolidation8
 
< 0.1%
Camping Membership6
 
< 0.1%
credit card debt consolidation3
 
< 0.1%
personal loan3
 
< 0.1%
credit card consolidation3
 
< 0.1%
Personal Loan3
 
< 0.1%
consolidate credit cards2
 
< 0.1%
I will use the loan and pay off all of my outstanding credit card debts that are spread out. It would be great to just focus on paying back one lender with a lower interest rate and not have to worry about keeping track each CC individually.2
 
< 0.1%
Borrower added on 12/05/11 > Credit Card Refinancing<br>2
 
< 0.1%
Other values (26517)26535
66.8%
(Missing)12940
32.6%
2021-04-14T11:21:16.308054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i77512
 
3.8%
to71096
 
3.5%
a54855
 
2.7%
the54340
 
2.7%
and54329
 
2.6%
my51308
 
2.5%
on49132
 
2.4%
37238
 
1.8%
for32774
 
1.6%
have32490
 
1.6%
Other values (53986)1535186
74.9%

Most occurring characters

ValueCountFrequency (%)
2121185
18.6%
e953964
 
8.4%
a714029
 
6.3%
o709013
 
6.2%
t649103
 
5.7%
n612137
 
5.4%
r589058
 
5.2%
i496003
 
4.3%
s426272
 
3.7%
d397984
 
3.5%
Other values (132)3751483
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8135704
71.2%
Space Separator2121258
 
18.6%
Decimal Number346663
 
3.0%
Other Punctuation326744
 
2.9%
Uppercase Letter302772
 
2.7%
Math Symbol140645
 
1.2%
Currency Symbol16745
 
0.1%
Dash Punctuation13032
 
0.1%
Close Punctuation7337
 
0.1%
Open Punctuation6727
 
0.1%
Other values (7)2604
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
I94559
31.2%
B34778
 
11.5%
T28474
 
9.4%
A15522
 
5.1%
C14303
 
4.7%
M14262
 
4.7%
S9642
 
3.2%
E9211
 
3.0%
W8830
 
2.9%
L8656
 
2.9%
Other values (21)64535
21.3%
ValueCountFrequency (%)
e953964
11.7%
a714029
 
8.8%
o709013
 
8.7%
t649103
 
8.0%
n612137
 
7.5%
r589058
 
7.2%
i496003
 
6.1%
s426272
 
5.2%
d397984
 
4.9%
l355636
 
4.4%
Other values (18)2232505
27.4%
ValueCountFrequency (%)
.120658
36.9%
/116441
35.6%
,50144
15.3%
'13317
 
4.1%
!6738
 
2.1%
%5704
 
1.7%
:5281
 
1.6%
;3357
 
1.0%
&2616
 
0.8%
"801
 
0.2%
Other values (10)1687
 
0.5%
ValueCountFrequency (%)
1287
60.5%
€411
 
19.3%
™191
 
9.0%
’38
 
1.8%
“37
 
1.7%
‚35
 
1.6%
27
 
1.3%
ƒ27
 
1.3%
œ23
 
1.1%
š15
 
0.7%
Other values (9)37
 
1.7%
ValueCountFrequency (%)
0104184
30.1%
197487
28.1%
236710
 
10.6%
521465
 
6.2%
317828
 
5.1%
916291
 
4.7%
413709
 
4.0%
613182
 
3.8%
712927
 
3.7%
812880
 
3.7%
ValueCountFrequency (%)
>84845
60.3%
<53870
38.3%
+984
 
0.7%
=615
 
0.4%
~290
 
0.2%
¬31
 
< 0.1%
|10
 
< 0.1%
ValueCountFrequency (%)
¦96
83.5%
©15
 
13.0%
®2
 
1.7%
2
 
1.7%
ValueCountFrequency (%)
(6681
99.3%
[44
 
0.7%
{2
 
< 0.1%
ValueCountFrequency (%)
)7290
99.4%
]44
 
0.6%
}3
 
< 0.1%
ValueCountFrequency (%)
-13017
99.9%
9
 
0.1%
6
 
< 0.1%
ValueCountFrequency (%)
`13
68.4%
^5
 
26.3%
¯1
 
5.3%
ValueCountFrequency (%)
2121185
> 99.9%
 73
 
< 0.1%
ValueCountFrequency (%)
$16671
99.6%
¢74
 
0.4%
ValueCountFrequency (%)
½6
75.0%
¾2
 
25.0%
ValueCountFrequency (%)
78
81.2%
18
 
18.8%
ValueCountFrequency (%)
18
85.7%
3
 
14.3%
ValueCountFrequency (%)
_217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8438476
73.9%
Common2981755
 
26.1%

Most frequent character per script

ValueCountFrequency (%)
2121185
71.1%
.120658
 
4.0%
/116441
 
3.9%
0104184
 
3.5%
197487
 
3.3%
>84845
 
2.8%
<53870
 
1.8%
,50144
 
1.7%
236710
 
1.2%
521465
 
0.7%
Other values (73)174766
 
5.9%
ValueCountFrequency (%)
e953964
 
11.3%
a714029
 
8.5%
o709013
 
8.4%
t649103
 
7.7%
n612137
 
7.3%
r589058
 
7.0%
i496003
 
5.9%
s426272
 
5.1%
d397984
 
4.7%
l355636
 
4.2%
Other values (49)2535277
30.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11418236
> 99.9%
None1834
 
< 0.1%
Punctuation159
 
< 0.1%
Specials2
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
2121185
18.6%
e953964
 
8.4%
a714029
 
6.3%
o709013
 
6.2%
t649103
 
5.7%
n612137
 
5.4%
r589058
 
5.2%
i496003
 
4.3%
s426272
 
3.7%
d397984
 
3.5%
Other values (86)3749488
32.8%
ValueCountFrequency (%)
â438
23.9%
€411
22.4%
™191
10.4%
Â127
 
6.9%
Ã97
 
5.3%
¦96
 
5.2%
¢74
 
4.0%
 73
 
4.0%
’38
 
2.1%
“37
 
2.0%
Other values (27)252
13.7%
ValueCountFrequency (%)
78
49.1%
19
 
11.9%
18
 
11.3%
18
 
11.3%
9
 
5.7%
8
 
5.0%
6
 
3.8%
3
 
1.9%
ValueCountFrequency (%)
2
100.0%

purpose
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
debt_consolidation
18641 
credit_card
5130 
other
3993 
home_improvement
2976 
major_purchase
2187 
Other values (9)
6790 

Length

Max length18
Median length16
Mean length13.7361835
Min length3

Characters and Unicode

Total characters545560
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowcar
3rd rowsmall_business
4th rowother
5th rowother
ValueCountFrequency (%)
debt_consolidation18641
46.9%
credit_card5130
 
12.9%
other3993
 
10.1%
home_improvement2976
 
7.5%
major_purchase2187
 
5.5%
small_business1828
 
4.6%
car1549
 
3.9%
wedding947
 
2.4%
medical693
 
1.7%
moving583
 
1.5%
Other values (4)1190
 
3.0%
2021-04-14T11:21:17.137654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation18641
46.9%
credit_card5130
 
12.9%
other3993
 
10.1%
home_improvement2976
 
7.5%
major_purchase2187
 
5.5%
small_business1828
 
4.6%
car1549
 
3.9%
wedding947
 
2.4%
medical693
 
1.7%
moving583
 
1.5%
Other values (4)1190
 
3.0%

Most occurring characters

ValueCountFrequency (%)
o69725
12.8%
d50454
9.2%
i50145
9.2%
t50087
9.2%
n44528
8.2%
e43568
 
8.0%
c34036
 
6.2%
a33730
 
6.2%
_30865
 
5.7%
s28521
 
5.2%
Other values (12)109901
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter514695
94.3%
Connector Punctuation30865
 
5.7%

Most frequent character per category

ValueCountFrequency (%)
o69725
13.5%
d50454
9.8%
i50145
9.7%
t50087
9.7%
n44528
8.7%
e43568
8.5%
c34036
 
6.6%
a33730
 
6.6%
s28521
 
5.5%
l23418
 
4.5%
Other values (11)86483
16.8%
ValueCountFrequency (%)
_30865
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin514695
94.3%
Common30865
 
5.7%

Most frequent character per script

ValueCountFrequency (%)
o69725
13.5%
d50454
9.8%
i50145
9.7%
t50087
9.7%
n44528
8.7%
e43568
8.5%
c34036
 
6.6%
a33730
 
6.6%
s28521
 
5.5%
l23418
 
4.5%
Other values (11)86483
16.8%
ValueCountFrequency (%)
_30865
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII545560
100.0%

Most frequent character per block

ValueCountFrequency (%)
o69725
12.8%
d50454
9.2%
i50145
9.2%
t50087
9.2%
n44528
8.2%
e43568
 
8.0%
c34036
 
6.2%
a33730
 
6.2%
_30865
 
5.7%
s28521
 
5.2%
Other values (12)109901
20.1%

title
Categorical

HIGH CARDINALITY

Distinct19615
Distinct (%)49.4%
Missing11
Missing (%)< 0.1%
Memory size310.4 KiB
Debt Consolidation
 
2184
Debt Consolidation Loan
 
1729
Personal Loan
 
659
Consolidation
 
517
debt consolidation
 
505
Other values (19610)
34112 

Length

Max length80
Median length16
Mean length17.18732685
Min length1

Characters and Unicode

Total characters682440
Distinct characters108
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17624 ?
Unique (%)44.4%

Sample

1st rowComputer
2nd rowbike
3rd rowreal estate business
4th rowpersonel
5th rowPersonal
ValueCountFrequency (%)
Debt Consolidation2184
 
5.5%
Debt Consolidation Loan1729
 
4.4%
Personal Loan659
 
1.7%
Consolidation517
 
1.3%
debt consolidation505
 
1.3%
Credit Card Consolidation356
 
0.9%
Home Improvement356
 
0.9%
Debt consolidation334
 
0.8%
Small Business Loan328
 
0.8%
Credit Card Loan317
 
0.8%
Other values (19605)32421
81.6%
2021-04-14T11:21:18.026649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
loan10895
 
10.4%
debt9245
 
8.8%
consolidation8622
 
8.2%
credit4604
 
4.4%
card3341
 
3.2%
personal2043
 
2.0%
home1875
 
1.8%
pay1344
 
1.3%
off1259
 
1.2%
my1133
 
1.1%
Other values (8935)60203
57.6%

Most occurring characters

ValueCountFrequency (%)
66029
 
9.7%
o65729
 
9.6%
n55657
 
8.2%
e54557
 
8.0%
a50167
 
7.4%
i43822
 
6.4%
t42600
 
6.2%
d30679
 
4.5%
r29153
 
4.3%
s28544
 
4.2%
Other values (98)215503
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter521300
76.4%
Uppercase Letter83242
 
12.2%
Space Separator66029
 
9.7%
Decimal Number5995
 
0.9%
Other Punctuation4442
 
0.7%
Dash Punctuation824
 
0.1%
Connector Punctuation213
 
< 0.1%
Close Punctuation104
 
< 0.1%
Currency Symbol94
 
< 0.1%
Math Symbol92
 
< 0.1%
Other values (5)105
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
C18509
22.2%
L10335
12.4%
D9244
11.1%
P5641
 
6.8%
R3732
 
4.5%
M3256
 
3.9%
S3227
 
3.9%
B3116
 
3.7%
H2910
 
3.5%
I2885
 
3.5%
Other values (18)20387
24.5%
ValueCountFrequency (%)
o65729
12.6%
n55657
10.7%
e54557
10.5%
a50167
9.6%
i43822
8.4%
t42600
8.2%
d30679
 
5.9%
r29153
 
5.6%
s28544
 
5.5%
l26300
 
5.0%
Other values (18)94092
18.0%
ValueCountFrequency (%)
!1123
25.3%
'982
22.1%
.712
16.0%
/538
12.1%
,435
 
9.8%
&328
 
7.4%
%95
 
2.1%
:64
 
1.4%
"56
 
1.3%
#25
 
0.6%
Other values (5)84
 
1.9%
ValueCountFrequency (%)
11691
28.2%
01677
28.0%
21105
18.4%
3299
 
5.0%
5256
 
4.3%
9254
 
4.2%
4216
 
3.6%
6178
 
3.0%
8169
 
2.8%
7150
 
2.5%
ValueCountFrequency (%)
€4
21.1%
4
21.1%
—4
21.1%
2
10.5%
™2
10.5%
–1
 
5.3%
‚1
 
5.3%
…1
 
5.3%
ValueCountFrequency (%)
+53
57.6%
=19
 
20.7%
<9
 
9.8%
>8
 
8.7%
~2
 
2.2%
|1
 
1.1%
ValueCountFrequency (%)
^1
33.3%
´1
33.3%
`1
33.3%
ValueCountFrequency (%)
(77
96.2%
[3
 
3.8%
ValueCountFrequency (%)
)100
96.2%
]4
 
3.8%
ValueCountFrequency (%)
66029
100.0%
ValueCountFrequency (%)
-824
100.0%
ValueCountFrequency (%)
_213
100.0%
ValueCountFrequency (%)
$94
100.0%
ValueCountFrequency (%)
³1
100.0%
ValueCountFrequency (%)
¦2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin604542
88.6%
Common77898
 
11.4%

Most frequent character per script

ValueCountFrequency (%)
o65729
 
10.9%
n55657
 
9.2%
e54557
 
9.0%
a50167
 
8.3%
i43822
 
7.2%
t42600
 
7.0%
d30679
 
5.1%
r29153
 
4.8%
s28544
 
4.7%
l26300
 
4.4%
Other values (46)177334
29.3%
ValueCountFrequency (%)
66029
84.8%
11691
 
2.2%
01677
 
2.2%
!1123
 
1.4%
21105
 
1.4%
'982
 
1.3%
-824
 
1.1%
.712
 
0.9%
/538
 
0.7%
,435
 
0.6%
Other values (42)2782
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII682408
> 99.9%
None32
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
66029
 
9.7%
o65729
 
9.6%
n55657
 
8.2%
e54557
 
8.0%
a50167
 
7.4%
i43822
 
6.4%
t42600
 
6.2%
d30679
 
4.5%
r29153
 
4.3%
s28544
 
4.2%
Other values (84)215471
31.6%
ValueCountFrequency (%)
â4
12.5%
€4
12.5%
î4
12.5%
4
12.5%
—4
12.5%
Ã2
6.2%
™2
6.2%
¦2
6.2%
–1
 
3.1%
‚1
 
3.1%
Other values (4)4
12.5%

zip_code
Categorical

HIGH CARDINALITY

Distinct823
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
100xx
 
597
945xx
 
545
112xx
 
516
606xx
 
503
070xx
 
473
Other values (818)
37083 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters198585
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)0.1%

Sample

1st row860xx
2nd row309xx
3rd row606xx
4th row917xx
5th row972xx
ValueCountFrequency (%)
100xx597
 
1.5%
945xx545
 
1.4%
112xx516
 
1.3%
606xx503
 
1.3%
070xx473
 
1.2%
900xx453
 
1.1%
021xx397
 
1.0%
300xx394
 
1.0%
926xx371
 
0.9%
750xx367
 
0.9%
Other values (813)35101
88.4%
2021-04-14T11:21:18.571158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100xx597
 
1.5%
945xx545
 
1.4%
112xx516
 
1.3%
606xx503
 
1.3%
070xx473
 
1.2%
900xx453
 
1.1%
021xx397
 
1.0%
300xx394
 
1.0%
926xx371
 
0.9%
750xx367
 
0.9%
Other values (813)35101
88.4%

Most occurring characters

ValueCountFrequency (%)
x79434
40.0%
019773
 
10.0%
115629
 
7.9%
213589
 
6.8%
912681
 
6.4%
312356
 
6.2%
710257
 
5.2%
49121
 
4.6%
59020
 
4.5%
88670
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number119151
60.0%
Lowercase Letter79434
40.0%

Most frequent character per category

ValueCountFrequency (%)
019773
16.6%
115629
13.1%
213589
11.4%
912681
10.6%
312356
10.4%
710257
8.6%
49121
7.7%
59020
7.6%
88670
7.3%
68055
6.8%
ValueCountFrequency (%)
x79434
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common119151
60.0%
Latin79434
40.0%

Most frequent character per script

ValueCountFrequency (%)
019773
16.6%
115629
13.1%
213589
11.4%
912681
10.6%
312356
10.4%
710257
8.6%
49121
7.7%
59020
7.6%
88670
7.3%
68055
6.8%
ValueCountFrequency (%)
x79434
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII198585
100.0%

Most frequent character per block

ValueCountFrequency (%)
x79434
40.0%
019773
 
10.0%
115629
 
7.9%
213589
 
6.8%
912681
 
6.4%
312356
 
6.2%
710257
 
5.2%
49121
 
4.6%
59020
 
4.5%
88670
 
4.4%

addr_state
Categorical

HIGH CORRELATION

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
CA
7099 
NY
3812 
FL
2866 
TX
2727 
NJ
 
1850
Other values (45)
21363 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters79434
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAZ
2nd rowGA
3rd rowIL
4th rowCA
5th rowOR
ValueCountFrequency (%)
CA7099
17.9%
NY3812
 
9.6%
FL2866
 
7.2%
TX2727
 
6.9%
NJ1850
 
4.7%
IL1525
 
3.8%
PA1517
 
3.8%
VA1407
 
3.5%
GA1398
 
3.5%
MA1340
 
3.4%
Other values (40)14176
35.7%
2021-04-14T11:21:19.042236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca7099
17.9%
ny3812
 
9.6%
fl2866
 
7.2%
tx2727
 
6.9%
nj1850
 
4.7%
il1525
 
3.8%
pa1517
 
3.8%
va1407
 
3.5%
ga1398
 
3.5%
ma1340
 
3.4%
Other values (40)14176
35.7%

Most occurring characters

ValueCountFrequency (%)
A15698
19.8%
C10116
12.7%
N7953
10.0%
L5279
 
6.6%
M4706
 
5.9%
Y4220
 
5.3%
T3892
 
4.9%
O3451
 
4.3%
I3097
 
3.9%
F2866
 
3.6%
Other values (14)18156
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter79434
100.0%

Most frequent character per category

ValueCountFrequency (%)
A15698
19.8%
C10116
12.7%
N7953
10.0%
L5279
 
6.6%
M4706
 
5.9%
Y4220
 
5.3%
T3892
 
4.9%
O3451
 
4.3%
I3097
 
3.9%
F2866
 
3.6%
Other values (14)18156
22.9%

Most occurring scripts

ValueCountFrequency (%)
Latin79434
100.0%

Most frequent character per script

ValueCountFrequency (%)
A15698
19.8%
C10116
12.7%
N7953
10.0%
L5279
 
6.6%
M4706
 
5.9%
Y4220
 
5.3%
T3892
 
4.9%
O3451
 
4.3%
I3097
 
3.9%
F2866
 
3.6%
Other values (14)18156
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII79434
100.0%

Most frequent character per block

ValueCountFrequency (%)
A15698
19.8%
C10116
12.7%
N7953
10.0%
L5279
 
6.6%
M4706
 
5.9%
Y4220
 
5.3%
T3892
 
4.9%
O3451
 
4.3%
I3097
 
3.9%
F2866
 
3.6%
Other values (14)18156
22.9%

dti
Real number (ℝ≥0)

Distinct2868
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.31512954
Minimum0
Maximum29.99
Zeros183
Zeros (%)0.5%
Memory size310.4 KiB
2021-04-14T11:21:19.256992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.13
Q18.17
median13.4
Q318.6
95-th percentile23.84
Maximum29.99
Range29.99
Interquartile range (IQR)10.43

Descriptive statistics

Standard deviation6.678593595
Coefficient of variation (CV)0.501579318
Kurtosis-0.8520154806
Mean13.31512954
Median Absolute Deviation (MAD)5.21
Skewness-0.02804333095
Sum528837
Variance44.6036124
MonotocityNot monotonic
2021-04-14T11:21:19.452036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0183
 
0.5%
1251
 
0.1%
1845
 
0.1%
19.240
 
0.1%
13.239
 
0.1%
16.838
 
0.1%
12.4838
 
0.1%
13.538
 
0.1%
637
 
0.1%
14.2936
 
0.1%
Other values (2858)39172
98.6%
ValueCountFrequency (%)
0183
0.5%
0.013
 
< 0.1%
0.025
 
< 0.1%
0.032
 
< 0.1%
0.043
 
< 0.1%
ValueCountFrequency (%)
29.991
 
< 0.1%
29.951
 
< 0.1%
29.933
< 0.1%
29.922
< 0.1%
29.891
 
< 0.1%

delinq_2yrs
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1465115694
Minimum0
Maximum11
Zeros35405
Zeros (%)89.1%
Memory size310.4 KiB
2021-04-14T11:21:19.640864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.491811516
Coefficient of variation (CV)3.356810102
Kurtosis39.41249957
Mean0.1465115694
Median Absolute Deviation (MAD)0
Skewness5.022035213
Sum5819
Variance0.2418785673
MonotocityNot monotonic
2021-04-14T11:21:19.819109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
035405
89.1%
13303
 
8.3%
2687
 
1.7%
3220
 
0.6%
462
 
0.2%
522
 
0.1%
610
 
< 0.1%
74
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
035405
89.1%
13303
 
8.3%
2687
 
1.7%
3220
 
0.6%
462
 
0.2%
ValueCountFrequency (%)
111
 
< 0.1%
91
 
< 0.1%
82
 
< 0.1%
74
 
< 0.1%
610
< 0.1%

earliest_cr_line
Categorical

HIGH CARDINALITY

Distinct526
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
Nov-98
 
370
Oct-99
 
366
Dec-98
 
348
Oct-00
 
346
Dec-97
 
329
Other values (521)
37958 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters238302
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)0.1%

Sample

1st rowJan-85
2nd rowApr-99
3rd rowNov-01
4th rowFeb-96
5th rowJan-96
ValueCountFrequency (%)
Nov-98370
 
0.9%
Oct-99366
 
0.9%
Dec-98348
 
0.9%
Oct-00346
 
0.9%
Dec-97329
 
0.8%
Nov-00320
 
0.8%
Nov-99319
 
0.8%
Sep-00306
 
0.8%
Oct-98305
 
0.8%
Nov-97298
 
0.8%
Other values (516)36410
91.7%
2021-04-14T11:21:20.305063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nov-98370
 
0.9%
oct-99366
 
0.9%
dec-98348
 
0.9%
oct-00346
 
0.9%
dec-97329
 
0.8%
nov-00320
 
0.8%
nov-99319
 
0.8%
sep-00306
 
0.8%
oct-98305
 
0.8%
nov-97298
 
0.8%
Other values (516)36410
91.7%

Most occurring characters

ValueCountFrequency (%)
-39717
16.7%
923353
 
9.8%
019365
 
8.1%
e10541
 
4.4%
J9426
 
4.0%
u9302
 
3.9%
a9126
 
3.8%
88453
 
3.5%
c8143
 
3.4%
n6364
 
2.7%
Other values (23)94512
39.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter79434
33.3%
Decimal Number79434
33.3%
Uppercase Letter39717
16.7%
Dash Punctuation39717
16.7%

Most frequent character per category

ValueCountFrequency (%)
e10541
13.3%
u9302
11.7%
a9126
11.5%
c8143
10.3%
n6364
8.0%
p6335
8.0%
r5536
7.0%
t4076
 
5.1%
o3930
 
4.9%
v3930
 
4.9%
Other values (4)12151
15.3%
ValueCountFrequency (%)
923353
29.4%
019365
24.4%
88453
 
10.6%
74822
 
6.1%
44274
 
5.4%
54201
 
5.3%
64174
 
5.3%
33784
 
4.8%
13736
 
4.7%
23272
 
4.1%
ValueCountFrequency (%)
J9426
23.7%
A6047
15.2%
M5697
14.3%
O4076
10.3%
D4067
10.2%
N3930
9.9%
S3593
 
9.0%
F2881
 
7.3%
ValueCountFrequency (%)
-39717
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin119151
50.0%
Common119151
50.0%

Most frequent character per script

ValueCountFrequency (%)
e10541
 
8.8%
J9426
 
7.9%
u9302
 
7.8%
a9126
 
7.7%
c8143
 
6.8%
n6364
 
5.3%
p6335
 
5.3%
A6047
 
5.1%
M5697
 
4.8%
r5536
 
4.6%
Other values (12)42634
35.8%
ValueCountFrequency (%)
-39717
33.3%
923353
19.6%
019365
16.3%
88453
 
7.1%
74822
 
4.0%
44274
 
3.6%
54201
 
3.5%
64174
 
3.5%
33784
 
3.2%
13736
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII238302
100.0%

Most frequent character per block

ValueCountFrequency (%)
-39717
16.7%
923353
 
9.8%
019365
 
8.1%
e10541
 
4.4%
J9426
 
4.0%
u9302
 
3.9%
a9126
 
3.8%
88453
 
3.5%
c8143
 
3.4%
n6364
 
2.7%
Other values (23)94512
39.7%

inq_last_6mths
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8691995871
Minimum0
Maximum8
Zeros19300
Zeros (%)48.6%
Memory size310.4 KiB
2021-04-14T11:21:20.470012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.070219332
Coefficient of variation (CV)1.23126995
Kurtosis2.562159858
Mean0.8691995871
Median Absolute Deviation (MAD)1
Skewness1.390390927
Sum34522
Variance1.145369419
MonotocityNot monotonic
2021-04-14T11:21:20.667869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
019300
48.6%
110971
27.6%
25812
 
14.6%
33048
 
7.7%
4326
 
0.8%
5146
 
0.4%
664
 
0.2%
735
 
0.1%
815
 
< 0.1%
ValueCountFrequency (%)
019300
48.6%
110971
27.6%
25812
 
14.6%
33048
 
7.7%
4326
 
0.8%
ValueCountFrequency (%)
815
 
< 0.1%
735
 
0.1%
664
 
0.2%
5146
0.4%
4326
0.8%

mths_since_last_delinq
Real number (ℝ≥0)

MISSING
ZEROS

Distinct95
Distinct (%)0.7%
Missing25682
Missing (%)64.7%
Infinite0
Infinite (%)0.0%
Mean35.90096188
Minimum0
Maximum120
Zeros443
Zeros (%)1.1%
Memory size310.4 KiB
2021-04-14T11:21:20.897531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q118
median34
Q352
95-th percentile75
Maximum120
Range120
Interquartile range (IQR)34

Descriptive statistics

Standard deviation22.02005955
Coefficient of variation (CV)0.6133556984
Kurtosis-0.8425777778
Mean35.90096188
Median Absolute Deviation (MAD)17
Skewness0.3064368727
Sum503870
Variance484.8830224
MonotocityNot monotonic
2021-04-14T11:21:21.137948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0443
 
1.1%
15252
 
0.6%
23247
 
0.6%
30247
 
0.6%
24241
 
0.6%
19238
 
0.6%
38237
 
0.6%
20233
 
0.6%
22231
 
0.6%
18231
 
0.6%
Other values (85)11435
28.8%
(Missing)25682
64.7%
ValueCountFrequency (%)
0443
1.1%
130
 
0.1%
2101
 
0.3%
3145
 
0.4%
4153
 
0.4%
ValueCountFrequency (%)
1201
< 0.1%
1151
< 0.1%
1071
< 0.1%
1061
< 0.1%
1032
< 0.1%

mths_since_last_record
Real number (ℝ≥0)

MISSING
ZEROS

Distinct111
Distinct (%)4.0%
Missing36931
Missing (%)93.0%
Infinite0
Infinite (%)0.0%
Mean69.69813352
Minimum0
Maximum129
Zeros670
Zeros (%)1.7%
Memory size310.4 KiB
2021-04-14T11:21:21.364497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122
median90
Q3104
95-th percentile115
Maximum129
Range129
Interquartile range (IQR)82

Descriptive statistics

Standard deviation43.82252903
Coefficient of variation (CV)0.6287475261
Kurtosis-1.156555701
Mean69.69813352
Median Absolute Deviation (MAD)20
Skewness-0.7172285764
Sum194179
Variance1920.41405
MonotocityNot monotonic
2021-04-14T11:21:21.613137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0670
 
1.7%
10461
 
0.2%
8960
 
0.2%
11359
 
0.1%
11157
 
0.1%
9455
 
0.1%
10855
 
0.1%
8754
 
0.1%
9354
 
0.1%
10053
 
0.1%
Other values (101)1608
 
4.0%
(Missing)36931
93.0%
ValueCountFrequency (%)
0670
1.7%
51
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
112
 
< 0.1%
ValueCountFrequency (%)
1291
 
< 0.1%
1201
 
< 0.1%
11910
 
< 0.1%
11836
0.1%
11747
0.1%

open_acc
Real number (ℝ≥0)

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.294407936
Minimum2
Maximum44
Zeros0
Zeros (%)0.0%
Memory size310.4 KiB
2021-04-14T11:21:21.892974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median9
Q312
95-th percentile17
Maximum44
Range42
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.400282474
Coefficient of variation (CV)0.4734333272
Kurtosis1.67757203
Mean9.294407936
Median Absolute Deviation (MAD)3
Skewness1.00376191
Sum369146
Variance19.36248585
MonotocityNot monotonic
2021-04-14T11:21:23.620620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
74018
10.1%
63946
9.9%
83936
9.9%
93718
9.4%
103223
 
8.1%
53183
 
8.0%
112746
 
6.9%
42343
 
5.9%
122273
 
5.7%
131911
 
4.8%
Other values (30)8420
21.2%
ValueCountFrequency (%)
2605
 
1.5%
31493
 
3.8%
42343
5.9%
53183
8.0%
63946
9.9%
ValueCountFrequency (%)
441
< 0.1%
421
< 0.1%
411
< 0.1%
391
< 0.1%
381
< 0.1%

pub_rec
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
0
37601 
1
 
2056
2
 
51
3
 
7
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39717
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
037601
94.7%
12056
 
5.2%
251
 
0.1%
37
 
< 0.1%
42
 
< 0.1%
2021-04-14T11:21:24.034371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:21:24.153900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
037601
94.7%
12056
 
5.2%
251
 
0.1%
37
 
< 0.1%
42
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
037601
94.7%
12056
 
5.2%
251
 
0.1%
37
 
< 0.1%
42
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39717
100.0%

Most frequent character per category

ValueCountFrequency (%)
037601
94.7%
12056
 
5.2%
251
 
0.1%
37
 
< 0.1%
42
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common39717
100.0%

Most frequent character per script

ValueCountFrequency (%)
037601
94.7%
12056
 
5.2%
251
 
0.1%
37
 
< 0.1%
42
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII39717
100.0%

Most frequent character per block

ValueCountFrequency (%)
037601
94.7%
12056
 
5.2%
251
 
0.1%
37
 
< 0.1%
42
 
< 0.1%

revol_bal
Real number (ℝ≥0)

ZEROS

Distinct21711
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13382.52809
Minimum0
Maximum149588
Zeros994
Zeros (%)2.5%
Memory size310.4 KiB
2021-04-14T11:21:24.468502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile321.8
Q13703
median8850
Q317058
95-th percentile41656.4
Maximum149588
Range149588
Interquartile range (IQR)13355

Descriptive statistics

Standard deviation15885.01664
Coefficient of variation (CV)1.186996697
Kurtosis14.89652278
Mean13382.52809
Median Absolute Deviation (MAD)6027
Skewness3.190883683
Sum531513868
Variance252333753.7
MonotocityNot monotonic
2021-04-14T11:21:24.685202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0994
 
2.5%
25514
 
< 0.1%
29814
 
< 0.1%
112
 
< 0.1%
68211
 
< 0.1%
7989
 
< 0.1%
3469
 
< 0.1%
109
 
< 0.1%
8659
 
< 0.1%
529
 
< 0.1%
Other values (21701)38627
97.3%
ValueCountFrequency (%)
0994
2.5%
112
 
< 0.1%
25
 
< 0.1%
36
 
< 0.1%
43
 
< 0.1%
ValueCountFrequency (%)
1495881
< 0.1%
1495271
< 0.1%
1490001
< 0.1%
1488291
< 0.1%
1488041
< 0.1%

revol_util
Categorical

HIGH CARDINALITY

Distinct1089
Distinct (%)2.7%
Missing50
Missing (%)0.1%
Memory size310.4 KiB
0%
 
977
0.20%
 
63
63%
 
62
66.70%
 
58
0.10%
 
58
Other values (1084)
38449 

Length

Max length6
Median length6
Mean length5.521919984
Min length2

Characters and Unicode

Total characters219038
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)0.2%

Sample

1st row83.70%
2nd row9.40%
3rd row98.50%
4th row21%
5th row53.90%
ValueCountFrequency (%)
0%977
 
2.5%
0.20%63
 
0.2%
63%62
 
0.2%
66.70%58
 
0.1%
0.10%58
 
0.1%
40.70%58
 
0.1%
46.40%57
 
0.1%
31.20%57
 
0.1%
66.60%57
 
0.1%
61%57
 
0.1%
Other values (1079)38163
96.1%
2021-04-14T11:21:25.192272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0977
 
2.5%
0.2063
 
0.2%
6362
 
0.2%
40.7058
 
0.1%
0.1058
 
0.1%
66.7058
 
0.1%
66.6057
 
0.1%
6157
 
0.1%
31.2057
 
0.1%
46.4057
 
0.1%
Other values (1079)38163
96.2%

Most occurring characters

ValueCountFrequency (%)
039671
18.1%
%39667
18.1%
.34841
15.9%
412082
 
5.5%
512063
 
5.5%
611989
 
5.5%
711949
 
5.5%
311885
 
5.4%
211550
 
5.3%
811419
 
5.2%
Other values (2)21922
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number144530
66.0%
Other Punctuation74508
34.0%

Most frequent character per category

ValueCountFrequency (%)
039671
27.4%
412082
 
8.4%
512063
 
8.3%
611989
 
8.3%
711949
 
8.3%
311885
 
8.2%
211550
 
8.0%
811419
 
7.9%
111111
 
7.7%
910811
 
7.5%
ValueCountFrequency (%)
%39667
53.2%
.34841
46.8%

Most occurring scripts

ValueCountFrequency (%)
Common219038
100.0%

Most frequent character per script

ValueCountFrequency (%)
039671
18.1%
%39667
18.1%
.34841
15.9%
412082
 
5.5%
512063
 
5.5%
611989
 
5.5%
711949
 
5.5%
311885
 
5.4%
211550
 
5.3%
811419
 
5.2%
Other values (2)21922
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII219038
100.0%

Most frequent character per block

ValueCountFrequency (%)
039671
18.1%
%39667
18.1%
.34841
15.9%
412082
 
5.5%
512063
 
5.5%
611989
 
5.5%
711949
 
5.5%
311885
 
5.4%
211550
 
5.3%
811419
 
5.2%
Other values (2)21922
10.0%

total_acc
Real number (ℝ≥0)

Distinct82
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.08882846
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Memory size310.4 KiB
2021-04-14T11:21:25.402388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q113
median20
Q329
95-th percentile43
Maximum90
Range88
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.40170855
Coefficient of variation (CV)0.5161753405
Kurtosis0.6937402027
Mean22.08882846
Median Absolute Deviation (MAD)7
Skewness0.8273790855
Sum877302
Variance129.9989579
MonotocityNot monotonic
2021-04-14T11:21:25.658791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161471
 
3.7%
151462
 
3.7%
171457
 
3.7%
141445
 
3.6%
201428
 
3.6%
181422
 
3.6%
211412
 
3.6%
131385
 
3.5%
191341
 
3.4%
121325
 
3.3%
Other values (72)25569
64.4%
ValueCountFrequency (%)
24
 
< 0.1%
3182
 
0.5%
4420
1.1%
5552
1.4%
6683
1.7%
ValueCountFrequency (%)
901
< 0.1%
871
< 0.1%
811
< 0.1%
801
< 0.1%
792
< 0.1%

initial_list_status
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
False
39717 
ValueCountFrequency (%)
False39717
100.0%
2021-04-14T11:21:25.799827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

out_prncp
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1137
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.2278873
Minimum0
Maximum6311.47
Zeros38577
Zeros (%)97.1%
Memory size310.4 KiB
2021-04-14T11:21:25.915097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6311.47
Range6311.47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation375.1728389
Coefficient of variation (CV)7.323605532
Kurtosis97.6585546
Mean51.2278873
Median Absolute Deviation (MAD)0
Skewness9.226730006
Sum2034618
Variance140754.659
MonotocityNot monotonic
2021-04-14T11:21:26.164482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
038577
97.1%
2277.112
 
< 0.1%
2963.242
 
< 0.1%
827.132
 
< 0.1%
1972.62
 
< 0.1%
1202.051
 
< 0.1%
4316.131
 
< 0.1%
3006.671
 
< 0.1%
1725.341
 
< 0.1%
743.521
 
< 0.1%
Other values (1127)1127
 
2.8%
ValueCountFrequency (%)
038577
97.1%
10.261
 
< 0.1%
11.911
 
< 0.1%
13.281
 
< 0.1%
19.121
 
< 0.1%
ValueCountFrequency (%)
6311.471
< 0.1%
6308.371
< 0.1%
6307.371
< 0.1%
6307.151
< 0.1%
6219.161
< 0.1%

out_prncp_inv
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1138
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.98976811
Minimum0
Maximum6307.37
Zeros38577
Zeros (%)97.1%
Memory size310.4 KiB
2021-04-14T11:21:26.407728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6307.37
Range6307.37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation373.8244569
Coefficient of variation (CV)7.331362169
Kurtosis98.04055348
Mean50.98976811
Median Absolute Deviation (MAD)0
Skewness9.243765495
Sum2025160.62
Variance139744.7246
MonotocityNot monotonic
2021-04-14T11:21:26.627497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
038577
97.1%
1972.62
 
< 0.1%
827.132
 
< 0.1%
1664.642
 
< 0.1%
1212.391
 
< 0.1%
1662.571
 
< 0.1%
3335.411
 
< 0.1%
3131.631
 
< 0.1%
272.651
 
< 0.1%
87.941
 
< 0.1%
Other values (1128)1128
 
2.8%
ValueCountFrequency (%)
038577
97.1%
10.261
 
< 0.1%
11.911
 
< 0.1%
13.281
 
< 0.1%
19.091
 
< 0.1%
ValueCountFrequency (%)
6307.371
< 0.1%
6306.961
< 0.1%
6298.111
< 0.1%
6276.751
< 0.1%
6219.161
< 0.1%

total_pymnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct37850
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12153.59654
Minimum0
Maximum58563.67993
Zeros16
Zeros (%)< 0.1%
Memory size310.4 KiB
2021-04-14T11:21:26.893653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1887.957036
Q15576.93
median9899.640319
Q316534.43304
95-th percentile30245.11853
Maximum58563.67993
Range58563.67993
Interquartile range (IQR)10957.50304

Descriptive statistics

Standard deviation9042.040766
Coefficient of variation (CV)0.743980659
Kurtosis1.985894249
Mean12153.59654
Median Absolute Deviation (MAD)5016.756711
Skewness1.339857366
Sum482704393.9
Variance81758501.21
MonotocityNot monotonic
2021-04-14T11:21:27.276250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11196.5694326
 
0.1%
016
 
< 0.1%
10956.7759616
 
< 0.1%
11784.2322316
 
< 0.1%
5478.38798115
 
< 0.1%
13148.1378615
 
< 0.1%
5557.02554313
 
< 0.1%
13435.9002113
 
< 0.1%
13263.9546412
 
< 0.1%
14288.7616911
 
< 0.1%
Other values (37840)39564
99.6%
ValueCountFrequency (%)
016
< 0.1%
33.731
 
< 0.1%
35.711
 
< 0.1%
44.922
 
< 0.1%
44.961
 
< 0.1%
ValueCountFrequency (%)
58563.679931
< 0.1%
58480.139921
< 0.1%
57835.279911
< 0.1%
56849.269861
< 0.1%
56662.589941
< 0.1%

total_pymnt_inv
Real number (ℝ≥0)

HIGH CORRELATION

Distinct37518
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11567.14912
Minimum0
Maximum58563.68
Zeros165
Zeros (%)0.4%
Memory size310.4 KiB
2021-04-14T11:21:27.741649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1420.408
Q15112.31
median9287.15
Q315798.81
95-th percentile29627.236
Maximum58563.68
Range58563.68
Interquartile range (IQR)10686.5

Descriptive statistics

Standard deviation8942.672613
Coefficient of variation (CV)0.7731094777
Kurtosis2.029758507
Mean11567.14912
Median Absolute Deviation (MAD)4939.58
Skewness1.35483764
Sum459412461.5
Variance79971393.47
MonotocityNot monotonic
2021-04-14T11:21:27.950142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0165
 
0.4%
6514.5216
 
< 0.1%
5478.3914
 
< 0.1%
13148.1414
 
< 0.1%
6717.9512
 
< 0.1%
10956.7812
 
< 0.1%
11196.5712
 
< 0.1%
5557.0311
 
< 0.1%
7328.9211
 
< 0.1%
13517.3611
 
< 0.1%
Other values (37508)39439
99.3%
ValueCountFrequency (%)
0165
0.4%
0.541
 
< 0.1%
12.651
 
< 0.1%
18.971
 
< 0.1%
21.61
 
< 0.1%
ValueCountFrequency (%)
58563.681
< 0.1%
58438.371
< 0.1%
57628.731
< 0.1%
56622.121
< 0.1%
56515.161
< 0.1%

total_rec_prncp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7976
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9793.348813
Minimum0
Maximum35000.02
Zeros74
Zeros (%)0.2%
Memory size310.4 KiB
2021-04-14T11:21:28.192675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1339.842
Q14600
median8000
Q313653.26
95-th percentile24999.982
Maximum35000.02
Range35000.02
Interquartile range (IQR)9053.26

Descriptive statistics

Standard deviation7065.522127
Coefficient of variation (CV)0.7214612961
Kurtosis1.103355455
Mean9793.348813
Median Absolute Deviation (MAD)4000
Skewness1.118254546
Sum388962434.8
Variance49921602.93
MonotocityNot monotonic
2021-04-14T11:21:28.447907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100002293
 
5.8%
120001805
 
4.5%
50001702
 
4.3%
60001637
 
4.1%
150001400
 
3.5%
80001318
 
3.3%
200001059
 
2.7%
4000956
 
2.4%
3000883
 
2.2%
7000851
 
2.1%
Other values (7966)25813
65.0%
ValueCountFrequency (%)
074
0.2%
21.211
 
< 0.1%
21.931
 
< 0.1%
22.241
 
< 0.1%
22.51
 
< 0.1%
ValueCountFrequency (%)
35000.022
 
< 0.1%
35000.011
 
< 0.1%
35000363
0.9%
34999.995
 
< 0.1%
34999.981
 
< 0.1%

total_rec_int
Real number (ℝ≥0)

Distinct35148
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2263.663172
Minimum0
Maximum23563.68
Zeros71
Zeros (%)0.2%
Memory size310.4 KiB
2021-04-14T11:21:28.694391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile186.168
Q1662.18
median1348.91
Q32833.4
95-th percentile7575.812
Maximum23563.68
Range23563.68
Interquartile range (IQR)2171.22

Descriptive statistics

Standard deviation2608.111964
Coefficient of variation (CV)1.152164331
Kurtosis9.688278395
Mean2263.663172
Median Absolute Deviation (MAD)866.01
Skewness2.668747187
Sum89905910.21
Variance6802248.019
MonotocityNot monotonic
2021-04-14T11:21:28.931863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
071
 
0.2%
1196.5726
 
0.1%
514.5219
 
< 0.1%
1784.2317
 
< 0.1%
717.9517
 
< 0.1%
1148.1417
 
< 0.1%
956.7817
 
< 0.1%
478.3916
 
< 0.1%
1907.3514
 
< 0.1%
632.2113
 
< 0.1%
Other values (35138)39490
99.4%
ValueCountFrequency (%)
071
0.2%
6.221
 
< 0.1%
6.271
 
< 0.1%
7.191
 
< 0.1%
7.22
 
< 0.1%
ValueCountFrequency (%)
23563.681
< 0.1%
23506.561
< 0.1%
23480.141
< 0.1%
22835.281
< 0.1%
22716.421
< 0.1%

total_rec_late_fee
Real number (ℝ≥0)

ZEROS

Distinct1356
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.363015212
Minimum0
Maximum180.2
Zeros37671
Zeros (%)94.8%
Memory size310.4 KiB
2021-04-14T11:21:29.165785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14.924199
Maximum180.2
Range180.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.289979302
Coefficient of variation (CV)5.348421085
Kurtosis100.8515437
Mean1.363015212
Median Absolute Deviation (MAD)0
Skewness8.429536
Sum54134.87519
Variance53.14379822
MonotocityNot monotonic
2021-04-14T11:21:29.396109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037671
94.8%
15255
 
0.6%
15.0000000158
 
0.1%
3055
 
0.1%
15.0000000247
 
0.1%
14.9999999940
 
0.1%
14.9999999833
 
0.1%
15.0000000332
 
0.1%
15.0000000425
 
0.1%
14.9999999725
 
0.1%
Other values (1346)1476
 
3.7%
ValueCountFrequency (%)
037671
94.8%
0.011
 
< 0.1%
0.0607997511
 
< 0.1%
0.0737871041
 
< 0.1%
0.1017045621
 
< 0.1%
ValueCountFrequency (%)
180.21
< 0.1%
166.42971071
< 0.1%
165.691
< 0.1%
146.60000031
< 0.1%
146.041
< 0.1%

recoveries
Real number (ℝ≥0)

ZEROS

Distinct4040
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.22162387
Minimum0
Maximum29623.35
Zeros35499
Zeros (%)89.4%
Memory size310.4 KiB
2021-04-14T11:21:29.637759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile362.418
Maximum29623.35
Range29623.35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation688.744771
Coefficient of variation (CV)7.23307105
Kurtosis379.3775773
Mean95.22162387
Median Absolute Deviation (MAD)0
Skewness16.5193782
Sum3781917.235
Variance474369.3595
MonotocityNot monotonic
2021-04-14T11:21:29.852190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035499
89.4%
10.44
 
< 0.1%
11.294
 
< 0.1%
10.663
 
< 0.1%
13.593
 
< 0.1%
13.933
 
< 0.1%
164.813
 
< 0.1%
19.23
 
< 0.1%
10.073
 
< 0.1%
10.133
 
< 0.1%
Other values (4030)4189
 
10.5%
ValueCountFrequency (%)
035499
89.4%
6.31
 
< 0.1%
6.311
 
< 0.1%
8.191
 
< 0.1%
8.361
 
< 0.1%
ValueCountFrequency (%)
29623.351
< 0.1%
22943.371
< 0.1%
21810.311
< 0.1%
20006.531
< 0.1%
19915.671
< 0.1%

collection_recovery_fee
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct2616
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.40611189
Minimum0
Maximum7002.19
Zeros35935
Zeros (%)90.5%
Memory size310.4 KiB
2021-04-14T11:21:30.078530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.152
Maximum7002.19
Range7002.19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation148.6715935
Coefficient of variation (CV)11.98373791
Kurtosis821.3006591
Mean12.40611189
Median Absolute Deviation (MAD)0
Skewness25.02941842
Sum492733.5461
Variance22103.2427
MonotocityNot monotonic
2021-04-14T11:21:30.286794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035935
90.5%
212
 
< 0.1%
1.210
 
< 0.1%
3.719
 
< 0.1%
1.888
 
< 0.1%
0.88
 
< 0.1%
1.698
 
< 0.1%
1.218
 
< 0.1%
2.028
 
< 0.1%
1.68
 
< 0.1%
Other values (2606)3703
 
9.3%
ValueCountFrequency (%)
035935
90.5%
0.0631
 
< 0.1%
0.0745000011
 
< 0.1%
0.1347999951
 
< 0.1%
0.13931
 
< 0.1%
ValueCountFrequency (%)
7002.191
< 0.1%
6972.591
< 0.1%
6543.041
< 0.1%
5774.81
< 0.1%
5602.721
< 0.1%

last_pymnt_d
Categorical

HIGH CARDINALITY

Distinct101
Distinct (%)0.3%
Missing71
Missing (%)0.2%
Memory size310.4 KiB
May-16
 
1256
Mar-13
 
1026
Dec-14
 
945
May-13
 
907
Feb-13
 
869
Other values (96)
34643 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters237876
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowJan-15
2nd rowApr-13
3rd rowJun-14
4th rowJan-15
5th rowMay-16
ValueCountFrequency (%)
May-161256
 
3.2%
Mar-131026
 
2.6%
Dec-14945
 
2.4%
May-13907
 
2.3%
Feb-13869
 
2.2%
Apr-13851
 
2.1%
Mar-12844
 
2.1%
Aug-14832
 
2.1%
Jan-14832
 
2.1%
Aug-12832
 
2.1%
Other values (91)30452
76.7%
2021-04-14T11:21:30.774136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may-161256
 
3.2%
mar-131026
 
2.6%
dec-14945
 
2.4%
may-13907
 
2.3%
feb-13869
 
2.2%
apr-13851
 
2.1%
mar-12844
 
2.1%
aug-12832
 
2.1%
jan-14832
 
2.1%
aug-14832
 
2.1%
Other values (91)30452
76.8%

Most occurring characters

ValueCountFrequency (%)
143946
18.5%
-39646
16.7%
a11087
 
4.7%
e9738
 
4.1%
39458
 
4.0%
u9401
 
4.0%
49269
 
3.9%
J9200
 
3.9%
28904
 
3.7%
M8046
 
3.4%
Other values (22)79181
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter79292
33.3%
Decimal Number79292
33.3%
Uppercase Letter39646
16.7%
Dash Punctuation39646
16.7%

Most frequent character per category

ValueCountFrequency (%)
a11087
14.0%
e9738
12.3%
u9401
11.9%
r6965
8.8%
c6783
8.6%
p6219
7.8%
n5974
7.5%
y4285
 
5.4%
t3271
 
4.1%
g3242
 
4.1%
Other values (4)12327
15.5%
ValueCountFrequency (%)
143946
55.4%
39458
 
11.9%
49269
 
11.7%
28904
 
11.2%
02544
 
3.2%
52431
 
3.1%
62044
 
2.6%
9559
 
0.7%
8137
 
0.2%
ValueCountFrequency (%)
J9200
23.2%
M8046
20.3%
A6446
16.3%
D3512
 
8.9%
O3271
 
8.3%
F3211
 
8.1%
S3015
 
7.6%
N2945
 
7.4%
ValueCountFrequency (%)
-39646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin118938
50.0%
Common118938
50.0%

Most frequent character per script

ValueCountFrequency (%)
a11087
 
9.3%
e9738
 
8.2%
u9401
 
7.9%
J9200
 
7.7%
M8046
 
6.8%
r6965
 
5.9%
c6783
 
5.7%
A6446
 
5.4%
p6219
 
5.2%
n5974
 
5.0%
Other values (12)39079
32.9%
ValueCountFrequency (%)
143946
36.9%
-39646
33.3%
39458
 
8.0%
49269
 
7.8%
28904
 
7.5%
02544
 
2.1%
52431
 
2.0%
62044
 
1.7%
9559
 
0.5%
8137
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII237876
100.0%

Most frequent character per block

ValueCountFrequency (%)
143946
18.5%
-39646
16.7%
a11087
 
4.7%
e9738
 
4.1%
39458
 
4.0%
u9401
 
4.0%
49269
 
3.9%
J9200
 
3.9%
28904
 
3.7%
M8046
 
3.4%
Other values (22)79181
33.3%

last_pymnt_amnt
Real number (ℝ≥0)

Distinct34930
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2678.826162
Minimum0
Maximum36115.2
Zeros74
Zeros (%)0.2%
Memory size310.4 KiB
2021-04-14T11:21:30.993139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.34
Q1218.68
median546.14
Q33293.16
95-th percentile12183.944
Maximum36115.2
Range36115.2
Interquartile range (IQR)3074.48

Descriptive statistics

Standard deviation4447.136012
Coefficient of variation (CV)1.660106234
Kurtosis8.867819694
Mean2678.826162
Median Absolute Deviation (MAD)449.45
Skewness2.712122241
Sum106394938.7
Variance19777018.71
MonotocityNot monotonic
2021-04-14T11:21:31.294982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
074
 
0.2%
276.0621
 
0.1%
20017
 
< 0.1%
5016
 
< 0.1%
10015
 
< 0.1%
40012
 
< 0.1%
773.4412
 
< 0.1%
15011
 
< 0.1%
786.0111
 
< 0.1%
50011
 
< 0.1%
Other values (34920)39517
99.5%
ValueCountFrequency (%)
074
0.2%
0.011
 
< 0.1%
0.021
 
< 0.1%
0.031
 
< 0.1%
0.131
 
< 0.1%
ValueCountFrequency (%)
36115.21
< 0.1%
35613.681
< 0.1%
35596.411
< 0.1%
35479.891
< 0.1%
35471.861
< 0.1%

next_pymnt_d
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing38577
Missing (%)97.1%
Memory size310.4 KiB
Jun-16
1125 
Jul-16
 
15

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6840
Distinct characters7
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJun-16
2nd rowJun-16
3rd rowJun-16
4th rowJun-16
5th rowJun-16
ValueCountFrequency (%)
Jun-161125
 
2.8%
Jul-1615
 
< 0.1%
(Missing)38577
97.1%
2021-04-14T11:21:31.821980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:21:31.977001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
jun-161125
98.7%
jul-1615
 
1.3%

Most occurring characters

ValueCountFrequency (%)
J1140
16.7%
u1140
16.7%
-1140
16.7%
11140
16.7%
61140
16.7%
n1125
16.4%
l15
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2280
33.3%
Decimal Number2280
33.3%
Uppercase Letter1140
16.7%
Dash Punctuation1140
16.7%

Most frequent character per category

ValueCountFrequency (%)
u1140
50.0%
n1125
49.3%
l15
 
0.7%
ValueCountFrequency (%)
11140
50.0%
61140
50.0%
ValueCountFrequency (%)
J1140
100.0%
ValueCountFrequency (%)
-1140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3420
50.0%
Common3420
50.0%

Most frequent character per script

ValueCountFrequency (%)
J1140
33.3%
u1140
33.3%
n1125
32.9%
l15
 
0.4%
ValueCountFrequency (%)
-1140
33.3%
11140
33.3%
61140
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII6840
100.0%

Most frequent character per block

ValueCountFrequency (%)
J1140
16.7%
u1140
16.7%
-1140
16.7%
11140
16.7%
61140
16.7%
n1125
16.4%
l15
 
0.2%

last_credit_pull_d
Categorical

HIGH CARDINALITY

Distinct106
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Memory size310.4 KiB
May-16
10308 
Apr-16
2547 
Mar-16
 
1123
Feb-13
 
843
Feb-16
 
736
Other values (101)
24158 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters238290
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowMay-16
2nd rowSep-13
3rd rowMay-16
4th rowApr-16
5th rowMay-16
ValueCountFrequency (%)
May-1610308
26.0%
Apr-162547
 
6.4%
Mar-161123
 
2.8%
Feb-13843
 
2.1%
Feb-16736
 
1.9%
Jan-16657
 
1.7%
Dec-15647
 
1.6%
Mar-13577
 
1.5%
Mar-14564
 
1.4%
Dec-14562
 
1.4%
Other values (96)21151
53.3%
2021-04-14T11:21:32.374228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may-1610308
26.0%
apr-162547
 
6.4%
mar-161123
 
2.8%
feb-13843
 
2.1%
feb-16736
 
1.9%
jan-16657
 
1.7%
dec-15647
 
1.6%
mar-13577
 
1.5%
mar-14564
 
1.4%
dec-14562
 
1.4%
Other values (96)21151
53.3%

Most occurring characters

ValueCountFrequency (%)
141601
17.5%
-39715
16.7%
a17601
 
7.4%
M15523
 
6.5%
615371
 
6.5%
y12231
 
5.1%
r7664
 
3.2%
e7600
 
3.2%
p6483
 
2.7%
A6411
 
2.7%
Other values (23)68090
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter79430
33.3%
Decimal Number79430
33.3%
Uppercase Letter39715
16.7%
Dash Punctuation39715
16.7%

Most frequent character per category

ValueCountFrequency (%)
a17601
22.2%
y12231
15.4%
r7664
9.6%
e7600
9.6%
p6483
 
8.2%
u5856
 
7.4%
c4475
 
5.6%
n3834
 
4.8%
b3075
 
3.9%
o2225
 
2.8%
Other values (4)8386
10.6%
ValueCountFrequency (%)
141601
52.4%
615371
 
19.4%
46255
 
7.9%
55502
 
6.9%
35164
 
6.5%
24079
 
5.1%
01153
 
1.5%
9228
 
0.3%
841
 
0.1%
736
 
< 0.1%
ValueCountFrequency (%)
M15523
39.1%
A6411
16.1%
J5895
 
14.8%
F3075
 
7.7%
D2414
 
6.1%
N2225
 
5.6%
S2111
 
5.3%
O2061
 
5.2%
ValueCountFrequency (%)
-39715
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin119145
50.0%
Common119145
50.0%

Most frequent character per script

ValueCountFrequency (%)
a17601
14.8%
M15523
13.0%
y12231
10.3%
r7664
 
6.4%
e7600
 
6.4%
p6483
 
5.4%
A6411
 
5.4%
J5895
 
4.9%
u5856
 
4.9%
c4475
 
3.8%
Other values (12)29406
24.7%
ValueCountFrequency (%)
141601
34.9%
-39715
33.3%
615371
 
12.9%
46255
 
5.2%
55502
 
4.6%
35164
 
4.3%
24079
 
3.4%
01153
 
1.0%
9228
 
0.2%
841
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII238290
100.0%

Most frequent character per block

ValueCountFrequency (%)
141601
17.5%
-39715
16.7%
a17601
 
7.4%
M15523
 
6.5%
615371
 
6.5%
y12231
 
5.1%
r7664
 
3.2%
e7600
 
3.2%
p6483
 
2.7%
A6411
 
2.7%
Other values (23)68090
28.6%

collections_12_mths_ex_med
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing56
Missing (%)0.1%
Memory size310.4 KiB
0.0
39661 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters118983
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.039661
99.9%
(Missing)56
 
0.1%
2021-04-14T11:21:32.726882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:21:32.831590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.039661
100.0%

Most occurring characters

ValueCountFrequency (%)
079322
66.7%
.39661
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number79322
66.7%
Other Punctuation39661
33.3%

Most frequent character per category

ValueCountFrequency (%)
079322
100.0%
ValueCountFrequency (%)
.39661
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common118983
100.0%

Most frequent character per script

ValueCountFrequency (%)
079322
66.7%
.39661
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII118983
100.0%

Most frequent character per block

ValueCountFrequency (%)
079322
66.7%
.39661
33.3%

mths_since_last_major_derog
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

policy_code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
1
39717 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39717
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
139717
100.0%
2021-04-14T11:21:33.090074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:21:33.198050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
139717
100.0%

Most occurring characters

ValueCountFrequency (%)
139717
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39717
100.0%

Most frequent character per category

ValueCountFrequency (%)
139717
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common39717
100.0%

Most frequent character per script

ValueCountFrequency (%)
139717
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII39717
100.0%

Most frequent character per block

ValueCountFrequency (%)
139717
100.0%

application_type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
INDIVIDUAL
39717 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters397170
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDIVIDUAL
2nd rowINDIVIDUAL
3rd rowINDIVIDUAL
4th rowINDIVIDUAL
5th rowINDIVIDUAL
ValueCountFrequency (%)
INDIVIDUAL39717
100.0%
2021-04-14T11:21:33.529135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:21:33.725692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
individual39717
100.0%

Most occurring characters

ValueCountFrequency (%)
I119151
30.0%
D79434
20.0%
N39717
 
10.0%
V39717
 
10.0%
U39717
 
10.0%
A39717
 
10.0%
L39717
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter397170
100.0%

Most frequent character per category

ValueCountFrequency (%)
I119151
30.0%
D79434
20.0%
N39717
 
10.0%
V39717
 
10.0%
U39717
 
10.0%
A39717
 
10.0%
L39717
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin397170
100.0%

Most frequent character per script

ValueCountFrequency (%)
I119151
30.0%
D79434
20.0%
N39717
 
10.0%
V39717
 
10.0%
U39717
 
10.0%
A39717
 
10.0%
L39717
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII397170
100.0%

Most frequent character per block

ValueCountFrequency (%)
I119151
30.0%
D79434
20.0%
N39717
 
10.0%
V39717
 
10.0%
U39717
 
10.0%
A39717
 
10.0%
L39717
 
10.0%

annual_inc_joint
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

dti_joint
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

verification_status_joint
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

acc_now_delinq
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
0
39717 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39717
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
039717
100.0%
2021-04-14T11:21:34.040794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:21:34.152958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
039717
100.0%

Most occurring characters

ValueCountFrequency (%)
039717
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39717
100.0%

Most frequent character per category

ValueCountFrequency (%)
039717
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common39717
100.0%

Most frequent character per script

ValueCountFrequency (%)
039717
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII39717
100.0%

Most frequent character per block

ValueCountFrequency (%)
039717
100.0%

tot_coll_amt
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

tot_cur_bal
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

open_acc_6m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

open_il_6m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

open_il_12m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

open_il_24m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mths_since_rcnt_il
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

total_bal_il
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

il_util
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

open_rv_12m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

open_rv_24m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

max_bal_bc
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

all_util
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

total_rev_hi_lim
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

inq_fi
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

total_cu_tl
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

inq_last_12m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

acc_open_past_24mths
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

avg_cur_bal
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

bc_open_to_buy
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

bc_util
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

chargeoff_within_12_mths
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing56
Missing (%)0.1%
Memory size310.4 KiB
0.0
39661 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters118983
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.039661
99.9%
(Missing)56
 
0.1%
2021-04-14T11:21:34.453348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:21:34.572105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.039661
100.0%

Most occurring characters

ValueCountFrequency (%)
079322
66.7%
.39661
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number79322
66.7%
Other Punctuation39661
33.3%

Most frequent character per category

ValueCountFrequency (%)
079322
100.0%
ValueCountFrequency (%)
.39661
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common118983
100.0%

Most frequent character per script

ValueCountFrequency (%)
079322
66.7%
.39661
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII118983
100.0%

Most frequent character per block

ValueCountFrequency (%)
079322
66.7%
.39661
33.3%

delinq_amnt
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
0
39717 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39717
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
039717
100.0%
2021-04-14T11:21:34.931204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:21:35.046441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
039717
100.0%

Most occurring characters

ValueCountFrequency (%)
039717
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39717
100.0%

Most frequent character per category

ValueCountFrequency (%)
039717
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common39717
100.0%

Most frequent character per script

ValueCountFrequency (%)
039717
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII39717
100.0%

Most frequent character per block

ValueCountFrequency (%)
039717
100.0%

mo_sin_old_il_acct
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mo_sin_old_rev_tl_op
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mo_sin_rcnt_rev_tl_op
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mo_sin_rcnt_tl
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mort_acc
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mths_since_recent_bc
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mths_since_recent_bc_dlq
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mths_since_recent_inq
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mths_since_recent_revol_delinq
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_accts_ever_120_pd
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_actv_bc_tl
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_actv_rev_tl
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_bc_sats
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_bc_tl
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_il_tl
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_op_rev_tl
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_rev_accts
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_rev_tl_bal_gt_0
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_sats
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_tl_120dpd_2m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_tl_30dpd
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_tl_90g_dpd_24m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_tl_op_past_12m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

pct_tl_nvr_dlq
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

percent_bc_gt_75
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

pub_rec_bankruptcies
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing697
Missing (%)1.8%
Memory size310.4 KiB
0.0
37339 
1.0
 
1674
2.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters117060
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.037339
94.0%
1.01674
 
4.2%
2.07
 
< 0.1%
(Missing)697
 
1.8%
2021-04-14T11:21:35.337240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:21:35.451930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.037339
95.7%
1.01674
 
4.3%
2.07
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
076359
65.2%
.39020
33.3%
11674
 
1.4%
27
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number78040
66.7%
Other Punctuation39020
33.3%

Most frequent character per category

ValueCountFrequency (%)
076359
97.8%
11674
 
2.1%
27
 
< 0.1%
ValueCountFrequency (%)
.39020
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common117060
100.0%

Most frequent character per script

ValueCountFrequency (%)
076359
65.2%
.39020
33.3%
11674
 
1.4%
27
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII117060
100.0%

Most frequent character per block

ValueCountFrequency (%)
076359
65.2%
.39020
33.3%
11674
 
1.4%
27
 
< 0.1%

tax_liens
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing39
Missing (%)0.1%
Memory size310.4 KiB
0.0
39678 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters119034
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.039678
99.9%
(Missing)39
 
0.1%
2021-04-14T11:21:35.753242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:21:35.876257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.039678
100.0%

Most occurring characters

ValueCountFrequency (%)
079356
66.7%
.39678
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number79356
66.7%
Other Punctuation39678
33.3%

Most frequent character per category

ValueCountFrequency (%)
079356
100.0%
ValueCountFrequency (%)
.39678
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common119034
100.0%

Most frequent character per script

ValueCountFrequency (%)
079356
66.7%
.39678
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII119034
100.0%

Most frequent character per block

ValueCountFrequency (%)
079356
66.7%
.39678
33.3%

tot_hi_cred_lim
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

total_bal_ex_mort
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

total_bc_limit
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

total_il_high_credit_limit
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

Interactions

2021-04-14T11:18:38.407983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:38.597345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:38.796237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:39.000300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:39.178898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:39.355366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:39.545338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:39.751763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:39.943315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:40.145948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:40.311693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:40.534184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:40.703323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:40.890914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:41.081291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:41.264980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:41.454538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:41.628135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:41.836534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:42.164234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:42.408897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:42.592325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:42.768374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:42.963092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:43.155592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:43.440314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:43.625024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:43.930657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:44.243834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:44.426572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:44.679006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:44.962462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:45.683053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:46.252647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:46.725253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:47.188886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:47.396550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:47.596960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:47.763719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:47.938403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:48.115344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:48.301952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:48.484009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:48.679091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:48.851574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:49.109163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:49.332153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:49.573064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:49.755519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:50.003097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:50.219507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:50.476640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:50.690714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:50.985629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:51.263453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:51.464158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:51.689795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:52.098830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:52.489935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:52.718089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:53.046711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:53.394466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:53.669467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:53.902554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:54.128435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:54.399295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:54.657194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:54.849037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:55.026261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:55.239090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:55.434578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:55.629460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:55.829823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:56.005318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:56.190010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:56.377522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:56.582848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:56.860174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:57.060820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:57.240746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:57.453460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:57.695236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:57.873817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:58.057351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:58.248398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:58.447357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:58.623602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:58.805922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:58.987472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:59.171060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:59.355154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:59.545706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:59.722766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:18:59.915727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:00.101543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:00.290621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:00.475963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:00.659941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:00.841488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:01.029476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:01.221570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:01.404216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:02.245427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:02.424771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:02.601458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:02.786965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:02.960521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:03.145991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:03.318959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:03.511160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:03.685736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:03.867702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:04.050759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:19:04.235743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-14T11:20:43.025107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-04-14T11:21:36.232301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-14T11:21:39.534754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-14T11:21:42.921681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-14T11:21:46.048399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-14T11:21:46.728938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-14T11:20:44.753250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-14T11:20:57.525426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-04-14T11:21:02.154861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-04-14T11:21:03.231808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idmember_idloan_amntfunded_amntfunded_amnt_invtermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspymnt_planurldescpurposetitlezip_codeaddr_statedtidelinq_2yrsearliest_cr_lineinq_last_6mthsmths_since_last_delinqmths_since_last_recordopen_accpub_recrevol_balrevol_utiltotal_accinitial_list_statusout_prncpout_prncp_invtotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_inttotal_rec_late_feerecoveriescollection_recovery_feelast_pymnt_dlast_pymnt_amntnext_pymnt_dlast_credit_pull_dcollections_12_mths_ex_medmths_since_last_major_derogpolicy_codeapplication_typeannual_inc_jointdti_jointverification_status_jointacc_now_delinqtot_coll_amttot_cur_balopen_acc_6mopen_il_6mopen_il_12mopen_il_24mmths_since_rcnt_iltotal_bal_ilil_utilopen_rv_12mopen_rv_24mmax_bal_bcall_utiltotal_rev_hi_liminq_fitotal_cu_tlinq_last_12macc_open_past_24mthsavg_cur_balbc_open_to_buybc_utilchargeoff_within_12_mthsdelinq_amntmo_sin_old_il_acctmo_sin_old_rev_tl_opmo_sin_rcnt_rev_tl_opmo_sin_rcnt_tlmort_accmths_since_recent_bcmths_since_recent_bc_dlqmths_since_recent_inqmths_since_recent_revol_delinqnum_accts_ever_120_pdnum_actv_bc_tlnum_actv_rev_tlnum_bc_satsnum_bc_tlnum_il_tlnum_op_rev_tlnum_rev_acctsnum_rev_tl_bal_gt_0num_satsnum_tl_120dpd_2mnum_tl_30dpdnum_tl_90g_dpd_24mnum_tl_op_past_12mpct_tl_nvr_dlqpercent_bc_gt_75pub_rec_bankruptciestax_lienstot_hi_cred_limtotal_bal_ex_morttotal_bc_limittotal_il_high_credit_limit
010775011296599500050004,975.0036 months10.65%162.87BB2NaN10+ yearsRENT24,000.00VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077501Borrower added on 12/22/11 > I need to upgrade my business technologies.<br>credit_cardComputer860xxAZ27.650Jan-851NaNNaN301364883.70%9f0.000.005,863.165,833.845,000.00863.160.000.000.00Jan-15171.62NaNMay-160.00NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000.00NaNNaNNaNNaN
110774301314167250025002,500.0060 months15.27%59.83CC4Ryder< 1 yearRENT30,000.00Source VerifiedDec-11Charged Offnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077430Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike. I only need this money because the deal im looking at is to good to pass up.<br><br> Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike.I only need this money because the deal im looking at is to good to pass up. I have finished college with an associates degree in business and its takingmeplaces<br>carbike309xxGA1.000Apr-995NaNNaN3016879.40%4f0.000.001,008.711,008.71456.46435.170.00117.081.11Apr-13119.66NaNSep-130.00NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000.00NaNNaNNaNNaN
210771751313524240024002,400.0036 months15.96%84.33CC5NaN10+ yearsRENT12,252.00Not VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077175NaNsmall_businessreal estate business606xxIL8.720Nov-012NaNNaN20295698.50%10f0.000.003,005.673,005.672,400.00605.670.000.000.00Jun-14649.91NaNMay-160.00NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000.00NaNNaNNaNNaN
310768631277178100001000010,000.0036 months13.49%339.31CC1AIR RESOURCES BOARD10+ yearsRENT49,200.00Source VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1076863Borrower added on 12/21/11 > to pay for property tax (borrow from friend, need to pay back) & central A/C need to be replace. I'm very sorry to let my loan expired last time.<br>otherpersonel917xxCA20.000Feb-96135.00NaN100559821%37f0.000.0012,231.8912,231.8910,000.002,214.9216.970.000.00Jan-15357.48NaNApr-160.00NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000.00NaNNaNNaNNaN
410753581311748300030003,000.0060 months12.69%67.79BB5University Medical Group1 yearRENT80,000.00Source VerifiedDec-11Currentnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1075358Borrower added on 12/21/11 > I plan on combining three large interest bills together and freeing up some extra each month to pay toward other bills. I've always been a good payor but have found myself needing to make adjustments to my budget due to a medical scare. My job is very stable, I love it.<br>otherPersonal972xxOR17.940Jan-96038.00NaN1502778353.90%38f524.06524.063,513.333,513.332,475.941,037.390.000.000.00May-1667.79Jun-16May-160.00NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000.00NaNNaNNaNNaN
510752691311441500050005,000.0036 months7.90%156.46AA4Veolia Transportaton3 yearsRENT36,000.00Source VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1075269NaNweddingMy wedding loan I promise to pay back852xxAZ11.200Nov-043NaNNaN90796328.30%12f0.000.005,632.215,632.215,000.00632.210.000.000.00Jan-15161.03NaNJan-160.00NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000.00NaNNaNNaNNaN
610696391304742700070007,000.0060 months15.96%170.08CC5Southern Star Photography8 yearsRENT47,004.00Not VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1069639Borrower added on 12/18/11 > I am planning on using the funds to pay off two retail credit cards with 24.99% interest rates, as well as a major bank credit card with a 18.99% rate. I pay all my bills on time, looking for a lower combined payment and lower monthly payment.<br>debt_consolidationLoan280xxNC23.510Jul-051NaNNaN701772685.60%11f0.000.0010,110.8410,110.846,985.613,125.230.000.000.00May-161,313.76NaNMay-160.00NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000.00NaNNaNNaNNaN
710720531288686300030003,000.0036 months18.64%109.43EE1MKC Accounting9 yearsRENT48,000.00Source VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1072053Borrower added on 12/16/11 > Downpayment for a car.<br>carCar Downpayment900xxCA5.350Jan-072NaNNaN40822187.50%4f0.000.003,939.143,939.143,000.00939.140.000.000.00Jan-15111.34NaNDec-140.00NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000.00NaNNaNNaNNaN
810717951306957560056005,600.0060 months21.28%152.39FF2NaN4 yearsOWN40,000.00Source VerifiedDec-11Charged Offnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1071795Borrower added on 12/21/11 > I own a small home-based judgment collection business. I have 5 years experience collecting debts. I am now going from a home office to a small office. I also plan to buy a small debt portfolio (eg. $10K for $1M of debt) <br>My score is not A+ because I own my home and have no mortgage.<br>small_businessExpand Business & Buy Debt Portfolio958xxCA5.550Apr-042NaNNaN110521032.60%13f0.000.00646.02646.02162.02294.940.00189.062.09Apr-12152.39NaNAug-120.00NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000.00NaNNaNNaNNaN
910715701306721537553755,350.0060 months12.69%121.45BB5Starbucks< 1 yearRENT15,000.00VerifiedDec-11Charged Offnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1071570Borrower added on 12/16/11 > I'm trying to build up my credit history. I live with my brother and have no car payment or credit cards. I am in community college and work full time. Im going to use the money to make some repairs around the house and get some maintenance done on my car.<br><br> Borrower added on 12/20/11 > $1000 down only $4375 to go. Thanks to everyone that invested so far, looking forward to surprising my brother with the fixes around the house.<br>otherBuilding my credit history.774xxTX18.080Sep-040NaNNaN20927936.50%3f0.000.001,476.191,469.34673.48533.420.00269.292.52Nov-12121.45NaNMar-130.00NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000.00NaNNaNNaNNaN

Last rows

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39707926669266150005000525.0036 months9.33%159.77BB3Stark and Roth Inc2 yearsMORTGAGE180,000.00Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92666Need a loan to make some home improvmentshome_improvementhome improvment loan530xxWI11.930Feb-9510.000.001606056839.20%38f0.000.005,751.53603.915,000.00751.530.000.000.00Jul-10161.55NaNJun-07NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39708925529254250005000375.0036 months9.96%161.25BB5Millenium Group4 yearsMORTGAGE48,000.00Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92552I would like to pay off my high-interest credit card debts and have a single payment to make every monthdebt_consolidationTito5000333xxFL8.030Aug-9510.000.00602832948.60%6f0.000.005,804.73435.365,000.00804.730.000.000.00Jul-10162.07NaNJun-10NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39709925339252950005000675.0036 months11.22%164.23CC4Self-Employeed< 1 yearOWN80,000.00Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92533NaNcredit_cardP's Family Credit Loan537xxWI1.210Jul-9630.0044.001512718516.10%29f0.000.005,912.05798.135,000.00912.050.000.000.00Jul-10165.17NaNJun-07NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39710925079250250005000250.0036 months7.43%155.38AA2Rush Univ Med Grp1 yearOWN85,000.00Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92507NaNcredit_cardMy Credit Card Loan537xxWI0.310Oct-9700.000.00702160.60%19f0.000.005,593.63279.685,000.00593.630.000.000.00Jul-10156.29NaNJun-07NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39711924029239050005000700.0036 months8.70%158.30BB1A. F. Wolfers, Inc.5 yearsMORTGAGE75,000.00Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92402I'd like to shift some credit card debt so it has a lower interest rate.credit_cardReduce Credit Card Debt804xxCO15.550May-9400.000.001006603323%29f0.000.005,698.60797.805,000.00698.600.000.000.00Jul-10159.83NaNNov-14NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
397129218792174250025001,075.0036 months8.07%78.42AA4FiSite Research4 yearsMORTGAGE110,000.00Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92187Our current gutter system on our home is old and in need of repair. We will be using the borrowed funds to replace the gutter system on our home.home_improvementHome Improvement802xxCO11.330Nov-9000.000.00130727413.10%40f0.000.002,822.971,213.882,500.00322.970.000.000.00Jul-1080.90NaNJun-10NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39713906659060785008500875.0036 months10.28%275.38CC1Squarewave Solutions, Ltd.3 yearsRENT18,000.00Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=90665The rate of interest and fees incurred by carrying a balance on my credit card are so outrageous at this point that continuing to pay them is patently bad financial thinking. I wish to redirect my efforts at retiring my debt via another more-reasonable means. I have sufficient funds to direct to this end on a monthly basis, and have simply gotten tired of their being gobbled up by interest and fees.credit_cardRetiring credit card debt274xxNC6.401Dec-8615.000.0060884726.90%9f0.000.009,913.491,020.518,500.001,413.490.000.000.00Jul-10281.94NaNJul-10NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
397149039590390500050001,325.0036 months8.07%156.84AA4NaN< 1 yearMORTGAGE100,000.00Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=90395NaNdebt_consolidationMBA Loan Consolidation017xxMA2.300Oct-9800.000.00110969819.40%20f0.000.005,272.161,397.125,000.00272.160.000.000.00Apr-080.00NaNJun-07NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39715903768924350005000650.0036 months7.43%155.38AA2NaN< 1 yearMORTGAGE200,000.00Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=90376NaNotherJAL Loan208xxMD3.720Nov-8800.000.00170856070.70%26f0.000.005,174.20672.665,000.00174.200.000.000.00Jan-080.00NaNJun-07NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39716870238699975007500800.0036 months13.75%255.43EE2Evergreen Center< 1 yearOWN22,000.00Not VerifiedJun-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=87023I plan to consolidate over $7,000 of debt: a combination of credit cards and student loans.debt_consolidationConsolidation Loan027xxMA14.291Oct-03011.000.0070417551.50%8f0.000.009,195.26980.837,500.001,695.260.000.000.00Jun-10256.59NaNJun-10NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN